At Notch, ADLC is the process behind every agentic AI system we build. Seven phases, Discovery through Integrate, with each producing a defined output before the next begins. Human oversight is mapped before production. Evaluation runs continuously after go-live. The methodology does not change with the project type, the team, or the scope.

SDLC governs deterministic systems. The same input produces the same output, every time. ADLC governs agentic systems, where behavior evolves based on context, prompts, models, and external tools. SDLC validates known code paths. ADLC evaluates reasoning quality and behavioral alignment. SDLC ends at deployment. ADLC treats deployment as the start of active monitoring, with evaluation running in production and feeding back into the build phase. The core difference is this: SDLC was built for software that executes. ADLC was built for software that thinks.

Agent Development Lifecycle is a structured methodology for building agentic AI systems. Where traditional software development assumes predictable execution, agentic AI systems reason, adapt, and act across tools and environments. ADLC organizes that complexity into defined phases, each with a clear output. Discovery, Design, Build, Agentic Experience, Human-in-the-Loop, Evaluate, and Integrate. Human oversight is designed in from the start. Evaluation covers reasoning quality, not just output accuracy.

The Agent Development Lifecycle (ADLC) is a structured methodology used by Notch for designing, building, evaluating, and deploying agentic AI systems. Agentic systems reason, adapt, and act across tools and environments. Small context changes compound into materially different outcomes. ADLC is built for that reality. Seven phases, each with a defined output. Human oversight designed in from the start. Evaluation that covers how the agent reasons, not just what it produces.

We follow the agent development lifecycle (ADLC), a six-phase lifecycle built for agentic systems. Each phase produces a defined output your team approves before the next begins, accounting for non-deterministic behavior and post-deployment evaluation loops.

Agentic AI systems go further than traditional automation. They make context-aware decisions, take actions, and interact with systems and people based on what is actually happening; not a fixed ruleset written for static conditions. When inputs are dynamic and unpredictable, agentic systems adapt. At Notch, every agentic system is built with human-in-the-loop controls defined before a single agent reaches production. Which actions run automatically. Which require approval. Which always need a human decision. That’s not a safety net. It’s an architectural standard.

A recruitment platform that receives a job brief, searches candidate databases, scores CVs against requirements, drafts outreach messages, and schedules interviews without a human coordinating each step. Each task is handled by a specialized agent. The agents delegate, share context, and recover from failure automatically. A human reviews and approves at defined points. The workflow runs end to end. That is agentic AI in practice. Notch built a system of this kind for Candid, cutting CV processing time by 75%.

Most initial implementations can be prototyped in 2–4 weeks, with full deployments typically completed within 6–8 weeks, depending on complexity and integration depth.

We implement a risk-based framework where only low-risk, well-defined decisions are handled by agents. Mid- and high-risk scenarios are escalated to humans, with clear logic and audit trails to maintain accountability.

Yes. We use agents to rapidly map your repositories and unstructured data, gaining a deep understanding of your systems. This frees our engineers to act as ideation partners, focusing on the “why” behind your specific domain rules.

We wrap AI models in a governed harness—a controlled environment of rules, tools, and checks that ensures agents stay on track. Our engineers follow a strict “AI proposes, human disposes” rule, meaning every line is verified and justified before commitment.

No. Our harness is model-agnostic by design, allowing you to switch to the latest best-in-class models without a system rewrite. We leverage the best-in-class tools and can adapt to new models without requiring a full system rewrite.

We use agents to generate and maintain realistic unit, integration, and end-to-end (E2E) tests alongside the code from day one. By making exhaustive testing a part of the everyday workflow, we reduce production incidents and ensure that new features don’t break existing business-critical paths.

For new greenfield projects, we use agents to quickly establish a clean, low-debt platform within our governed framework. For legacy systems, our agents rapidly explore and refactor codebases, enabling us to reduce existing technical debt with confidence.

We combine strict, human-written specifications with CI-integrated harness checks and automated regression protections. This keeps code transparent and manageable, preventing the accumulation of accidental technical debt.

Yes. You retain 100% IP ownership. We deliver a transparent, governable AI layer designed for human auditability and long-term internal maintenance.

Agents systematically traverse your repositories to map dependencies and summarize components, reducing onboarding from weeks to days. This allows us to refactor old debt and modernize platforms with high confidence.

Our process prioritizes Technical Design Thinking and long-term maintainability over simple feature output. By using agents to refactor old debt and modernize platforms faster, we ensure your product remains an evolving asset, not a static tool.

Agentic experience, or AX, is a design discipline focused on how humans and AI agents share a workflow, covering how people delegate tasks, receive outcomes, and maintain trust, while agents perceive and navigate interfaces and collaborate with users.  The term was introduced by Mathias Biilmann of Netlify.

Traditional UX is deterministic, while agentic experience introduces probabilistic behavior – an AI agent may take actions autonomously, and the design must account for transparency, override controls, and trust-building patterns that don’t exist in conventional interface design.

In addition to the core UX skills – interaction design, user research, information architecture, and UX writing – the new ones are a conceptual understanding of LLMs, designing for uncertain and probabilistic outcomes, and prompt design as a content discipline.

Earlier is better, but it’s rarely how it happens. Ideally, AX runs alongside agent development so the interaction model is designed against real system behavior. That said, AX is also the right response when an agent is already deployed but adoption is lower than expected; it’s as much a diagnostic discipline as a design one.

The most common failure is that users don’t adopt the agent or quietly work around it. They revert to manual tasks because they don’t have a clear mental model of what the agent will do, can’t tell when it’s acted, and don’t know how to recover if something goes wrong. AX addresses exactly that gap.

Usability testing, task analysis, and interviews all transfer, but the questions change. In conventional UX research, you’re testing whether users can complete a task. In AX, you’re testing whether users understand what the agent did, whether they trust it to do it again, and whether they know how to step in when it’s wrong.

The UX/UI designer owns what the user sees – the interface layer across screen, chat, and voice. The AX designer owns the conditions under which the agent acts and how trust is earned over time. In practice, they work on the same surfaces but from different starting points: one from the user’s mental model, one from the agent’s behavior.

Wireframes and prototypes still apply, but AX adds deliverables that don’t exist in conventional UX: trust architecture maps that define what the agent handles autonomously vs. what needs a confirmation step, and intervention design specs that describe exactly what users see when the agent acts, fails, or needs input.

An AI Discovery Workshop identifies AI opportunities and how they can benefit your business.

Typically one to two days.

Yes, it can be combined with AI engineering and business consulting.

Deliverables include AI opportunity reports

We customize the workshop based on your industry, goals, and data availability.

Business leaders, data scientists, and product managers should participate.

We identify opportunities in automation, data analysis, customer experience, and more.

It helps uncover AI use cases that improve efficiency and innovation.

AI Engineering is the design, development, and deployment of artificial intelligence solutions tailored to business needs.

We implement robust security measures and comply with data protection laws throughout the project.

Yes, we design AI solutions to integrate seamlessly with your current technology stack.

We follow ethical guidelines, ensure transparency, and comply with relevant data privacy regulations.

Deliverables include trained AI models, integration APIs, documentation, and deployment support.

We start with problem definition, followed by data preparation, model development, testing, and deployment.

We build solutions for automation, data analysis, natural language processing, computer vision, and more.

AI Engineering focuses on building and integrating AI systems, while AI Discovery identifies opportunities and use cases.

An AI MVP is a simplified version of your AI-powered product idea that integrates artificial intelligence from the start to test core concepts, validate business models, and collect real user feedback before committing resources to full-scale development.

AI MVPs need some form of relevant data to train and validate models, require continuous iteration based on AI performance metrics, involve specialized AI expertise for model selection and optimization, and focus on validating both technical feasibility and user acceptance of intelligent features rather than just basic functionality.

While having your own quality data provides the best results, AI MVPs can start with synthetic data, open datasets, or pre-trained models. The key is ensuring any data used is relevant, clean, and sufficient to validate your core AI hypothesis.

AI MVP validation involves A/B testing model performance, measuring accuracy against user expectations, collecting feedback on AI-generated outputs, monitoring user engagement with intelligent features, and iterating based on real-world usage patterns.

Major risks include poor data quality leading to inaccurate results, unrealistic performance expectations, users not trusting AI recommendations, model bias impacting outcomes, and underestimating the complexity of real-world AI behavior.

Yes, modern AI MVPs can leverage pre-trained models, APIs from providers like OpenAI or Google, and no-code AI platforms to integrate intelligent features without building custom machine learning models from scratch.

The process typically involves defining AI opportunities, rapid prototyping with minimal code, continuous user testing and model refinement, weekly stakeholder reviews, live deployment for behavioral insights, and iterative improvements based on real usage data.

Success metrics include model accuracy improvements, user adoption of AI features, task completion rates, user satisfaction with AI outputs, business impact measurements, and technical performance indicators like response time and system reliability.

Validated AI features can be scaled into production-ready systems with enhanced models, expanded datasets, improved accuracy, additional use cases, enterprise-grade infrastructure, and integration with broader business processes.

Healthcare, financial services, e-commerce, customer service, education, and manufacturing see strong AI MVP adoption due to clear automation opportunities, available data sources, and measurable improvements in efficiency and user experience.

An AI POC is a small-scale experiment with limited scope, designed to test the hypothesis and feasibility of an AI solution for a particular business problem before full deployment.

You need to clearly define the business problem, set success criteria, and ensure access to relevant, quality data to enable focused and effective AI validation.

The best times to consider an AI POC include testing if AI is the right approach to solving a particular business problem or exploring AI tool integration before further commitments.

Our service focuses on risk reduction and avoiding costly mistakes, so negative findings still provide valuable strategic clarity for qualifying or disqualifying ideas.

Not at all. Our service enables the integration of advanced AI features, making them accessible, understandable, and engaging for end-users with varying technical backgrounds.

The POC provides measurable outcomes and concrete results that can be used to justify investments in full-scale development.

You receive a comprehensive report including performance results, fine-tuning approaches, architectural and design decisions, and operational cost projections for scaling.

Proof of concepting begins by identifying specific business challenges and includes real usage insights to assess practicality for your specific context.

Your involvement is valuable. Our process includes early user feedback sessions and continuous implementation of findings.

Our service includes building a light, working version for functionality and technical fit validation, not just theoretical analysis.

AI POC includes selecting, fine-tuning, and evaluating large pre-trained models while also experimenting with custom approaches and techniques.

The service includes recommendations for future scaling and production use as part of aligning AI capabilities with business goals.

The process involves the continuous implementation of findings, emphasizing innovation and agility to deliver better products.

Notch uses AI across the software development lifecycle (SDLC) while keeping senior teams accountable for architecture, quality, security, and business fit. Our AI-native development approach is guided by the NotchForge™ framework, our agentic platform accelerator, Sovera AI™, the Notch 3S methodology for fast greenfield MVP development, and production-grade engineering practices.

No. AI-driven SDLC can support both greenfield software development and existing digital products. The same approach helps teams plan, validate, improve, and evolve software across the full lifecycle.

After launch, AI agents help analyze usage data, synthesize production feedback, and surface patterns in incidents or user behavior. Teams can refine live features faster and with clearer evidence.

AI supports QA by generating test scenarios, expanding coverage, identifying inconsistencies, and helping detect regression risk earlier. Testing becomes a continuous part of the software development lifecycle, not only a late-stage checkpoint.

Yes. AI can help analyze legacy systems, surface dependencies, map business logic, and identify technical risk before teams make major changes.

AI helps teams analyze existing systems, workflows, documentation, user behavior, and technical constraints during discovery. This makes requirements clearer and keeps business intent connected to user flows, architecture, test coverage, and delivery decisions.

No. AI agents support repeatable tasks, checks, documentation, and testing, while engineers stay accountable for architecture, security, quality, and long-term maintainability.

Traditional SDLC often moves through separate phases with heavy handoffs between teams. AI-driven software development lifecycle (SDLC) brings greater automation, context, and feedback to each stage, making software delivery more connected and easier to improve.

An AI-driven SDLC is a software development lifecycle where AI agents support planning, discovery, design, development, testing, deployment, and post-launch improvement. It helps teams reduce handoffs, automate repeatable work, and maintain business intent throughout delivery.

An end-to-end AI engineering partner covers the full lifecycle – from identifying where AI creates business value, through design and architecture, to building, deploying, and governing production AI systems. Unlike consultancies that stop at strategy or vendors that hand off at delivery, an end-to-end partner owns the outcome across every stage.

An agentic AI system is software that can perceive context, make decisions, and take actions autonomously – executing multi-step workflows with minimal human intervention. Unlike traditional automation, agentic systems can handle ambiguity, adapt to new information, and collaborate with other agents. They’re used to automate complex business processes across operations, customer interactions, and data workflows.

Scope and complexity determine timeline, but a well-structured engagement typically moves from discovery to initial deployment in 8–16 weeks. A scoping phase (2–3 weeks) defines the problem, data requirements, and governance model. Build phases follow a structured sprint schedule, with working components delivered throughout, not just at the end.

An AI proof of concept validates that a technical approach works in a controlled setting – usually with sample data and limited scope. A production-ready AI system handles real data volumes, integrates with live systems, includes monitoring and governance, and is built to stay accurate as your business evolves. Many proofs of concept fail in production because they weren’t designed with operational requirements in mind from the start.

AI-assisted legacy modernization uses coding agents to map and analyze the existing codebase first – compressing weeks of archaeology into days. Refactoring is done surgically, with automated tests at each step to preserve business logic. Work is sequenced so that core operations continue to run throughout. The result is a modernized architecture without a big-bang cutover.

Production agentic systems require human oversight checkpoints, audit trails, defined escalation paths, and monitoring for drift or unexpected behavior. Governance should be designed into the system architecture from the start – not added after deployment. This includes role-based access, decision logging, fallback states, and clear accountability for system actions. We use Sovera AI™ and NotchForge™ to accelerate the engineering work.

A Model Development Lifecycle (MDLC) is a structured methodology for building, evaluating, deploying, and maintaining AI models in production. It covers data preparation, model selection, training, validation, deployment pipelines, and ongoing monitoring – ensuring models stay accurate as underlying data and business requirements evolve. Without a formal MDLC, models degrade silently over time.

No. AI coding agents accelerate engineering work – handling repetitive code generation, test writing, and documentation – but senior engineers provide the judgment, architecture decisions, and business context that determine whether software actually solves the right problem. In practice, the best outcomes come from senior engineers directing coding agents, not from removing engineers from the process

ROI from AI engineering is measured against specific operational baselines – processing time, error rates, headcount per unit of output, or cycle time. Defining these baselines during the scoping phase is essential. Common measurable outcomes include reductions in manual workflow time (often 50–75%), faster document processing, and shorter time-to-production for engineering teams.

Before committing to build, it’s worth running an AI Discovery or AI Audit to identify where AI creates the most value relative to implementation complexity, assess data readiness, and align stakeholders on success criteria. Skipping this step is the most common reason AI projects stall mid-delivery or fail to reach production.

We don’t deliver against the spec and walk away. We take responsibility for the outcome, which means staying involved until what we built actually solves the problem it was designed for.

We work with funded scaleups and ambitious SMEs — companies building serious AI products who need a cross-functional partner, not just a development shop.

Every line of AI-generated code is reviewed and signed off by a senior engineer before it’s committed. We only ship code we can fully explain and stand behind.

Legacy complexity is something we’re set up for. We map your existing systems systematically and modernise them without disrupting the business logic that runs your operations.

You do. 100%. We deliver a transparent, fully auditable codebase built for your team to own, maintain, and extend — independently of us.

No. Our architecture is model-agnostic by design. As the market evolves and better models emerge, you can switch without rewriting your system.

Every engagement starts with AI Discovery — sessions that surface your specific business logic, domain rules, and edge cases. Everything we build is modeled around how your business actually works.

Business Process Consulting involves analyzing, optimizing, and redesigning business processes to improve efficiency, profitability, and long-term success.

By involving stakeholders, aligning with business goals, and focusing on achievable, measurable changes.

Yes, business process consultants often support automation initiatives and digital transformation projects.

Success is measured by improved efficiency, cost savings, increased productivity, and achievement of predefined KPIs.

Deliverables may include process maps, optimization recommendations, implementation plans, and performance metrics.

It generally involves assessment, mapping current processes, identifying bottlenecks, recommending improvements, and supporting implementation.

Consider their industry experience, problem-solving skills, communication abilities, and track record with similar clients.

A consultant brings expertise to identify inefficiencies, implement best practices, and drive measurable improvements in your operations.

It involves connecting your business processes and systems to the Camunda platform for workflow and decision automation.

We provide training, maintenance, and ongoing optimization to ensure your processes run smoothly.

We implement Camunda’s monitoring tools, set up KPIs, and provide dashboards for real-time insights.

Deliverables include configured Camunda BPM, process models, integration scripts, and documentation.

Camunda automates workflows using BPMN models, enabling rules, conditions, and actions to be executed automatically.

Yes, Camunda offers REST APIs, connectors, and supports integration with databases, messaging systems, and cloud platforms.

Key features include BPMN and DMN support, a powerful workflow engine, analytics, and developer-friendly APIs.

Benefits include process automation, improved efficiency, scalability, and real-time monitoring of workflows.

Custom Software Development is the process of designing, building, and deploying software tailored to specific business needs.

We use agile methodologies, regular progress tracking, and transparent communication to keep projects on track.

Yes, we have experience with compliance in regulated industries such as healthcare and finance.

We use code reviews, automated testing, and adhere to industry security standards throughout development.

Deliverables include technical specifications, source code, documentation, and user manuals.

A cross-functional team including project managers, developers, designers, and QA engineers.

We follow a structured process: requirements gathering, design, development, testing, deployment, and support.

Custom software is built for your unique requirements, offering flexibility and scalability that packaged solutions may lack.

A Design System is a collection of reusable components, guidelines, and standards that ensure consistency across digital products.

By enforcing guidelines, conducting audits, and providing training for teams using the system.

Benefits include faster design cycles, easier scaling, reduced inconsistencies, and improved user experience.

Yes, we can retrofit and integrate design systems into legacy and new applications.

A dedicated design system team or product managers typically oversee ongoing maintenance and updates.

We collaborate with cross-functional teams to define, build, and roll out the system across products.

Components include UI elements, design tokens, documentation, usage guidelines, and accessibility standards.

It streamlines design and development, ensures brand consistency, and accelerates product delivery.

An Ideation Workshop is a structured session designed to generate and evaluate new ideas for products, services, or internal improvements, using creative techniques and group collaboration.

Deliverables typically include a list of generated ideas, prioritized concepts, and next steps for validation or prototyping.

Common tools include brainstorming cards, analogy thinking, warm-up exercises, and structured group activities.

Workshops can range from a few hours to a full day, depending on the agenda and objectives.

1. There are no bad ideas.

2. Capture everything.

3. Go for hybrid brainstorming.

4. Quantity over quality.

The main goals are to generate a wide range of ideas, encourage creative thinking, and identify promising concepts for further development.

Participants typically include stakeholders, subject matter experts, designers, and facilitators to ensure diverse perspectives and expertise.

Ideation is not the first step; it follows empathy research and problem definition to ensure ideas address real user needs.

Legacy application modernization is the process of updating legacy applications to modern platforms and technologies to improve performance, scalability, and integration.

Notch combines agentic development with disciplined refactoring and technical debt reduction—led by senior engineers, not junior teams supervised by AI. Our described approach proves legacy modernization value before you commit to a larger engagement.

Traditional rewrites treat legacy modernization as a single, multi-year bet. An agentic approach treats it as a portfolio of small, testable improvements. Each slice delivers value independently, so the business never depends on a far-off “go-live” date.

It’s an approach where senior engineers orchestrate agentic AI to understand, wrap, and incrementally improve legacy systems, but without replacing them wholesale. Agents automate discovery, testing, and impact analysis while humans make the architectural and business decisions.

Yes, we support both migrating existing systems and redesigning applications for new requirements.

We implement modern security protocols and align with industry regulations throughout the project.

Deliverables include modernization roadmaps, updated applications, integration plans, and documentation.

We plan phased rollouts, thorough testing, and maintain parallel systems during transitions.

Challenges include data migration, integration with existing systems, and minimizing business disruption.

Approaches include re-platforming, re-architecting, cloud migration, and incremental upgrades.

Modernization reduces maintenance costs, improves agility, enhances user experience, and addresses security and compliance risks.

An MVP is an initial version of a product with just enough features to satisfy early adopters and gather feedback for future development.

Delivery time varies but typically ranges from a few weeks to a few months, depending on scope.

Yes, we support ongoing development and scaling based on validated learning from the MVP.

We collect feedback through analytics and user interviews, then iterate the product accordingly.

Deliverables include a functional MVP, user documentation, and a roadmap for future enhancements.

Features are prioritized based on user needs, business goals, and technical feasibility.

We identify essential features, develop rapidly, and iterate based on real user feedback.

An MVP focuses on core features to validate assumptions, while a full product includes all planned functionalities.

Model development lifecycle (MDLC) represents an end-to-end approach to building and running AI models, from defining the problem and shaping raw data, through training, error analysis, and monitoring in production. 

The most important thing is clarity on the problem: what the model needs to decide or predict, and what good output looks like. Data doesn’t have to be perfect, but it has to exist and be workable.

General-purpose AI tools cover a lot of ground, but not a specific ground. When the task is niche, the data is proprietary, or the stakes of a wrong output are high, a model tuned specifically to a certain domain will consistently outperform one built for everyone. 

By planning for drift and retraining from day one. Production performance is continuously monitored, errors are clustered by pattern, and the learned lessons feed back into the next iteration. 

It comes down to the data situation, how much control is needed over infrastructure, governance constraints, and the nature of the task. Frontier models are a strong fit when speed matters and the domain is well-covered. Custom models make sense when the data is proprietary, regulatory requirements demand local reasoning, or off-the-shelf models simply don’t understand the domain well enough.

An end-to-end AI engineering partner covers the full lifecycle – from identifying where AI creates business value, through design and architecture, to building, deploying, and governing production AI systems. Unlike consultancies that stop at strategy or vendors that hand off at delivery, an end-to-end partner owns the outcome across every stage.

A greenfield project is a new software product built from scratch – while respecting organizational-wide architectural standards and available data sources. Unlike a rebuild or modernization, you’re not working around existing systems. The architecture, stack, and AI layer are all designed for current needs from day one. You get software that is more adaptive, resilient, and aligned with your long-term business needs.

The same team stays on. The Strengthen phase runs two parallel tracks: Harden (performance, security, observability) and Evolve (next user journeys informed by real behavior). Monthly checkpoints keep priorities aligned. The goal is a product that keeps improving, not a handoff and a goodbye.

Before committing to build, it’s worth running an AI Discovery or AI Audit to identify where AI creates the most value relative to implementation complexity, assess data readiness, and align stakeholders on success criteria. Skipping this step is the most common reason AI projects stall mid-delivery or fail to reach production.

Collaboration is structured into every phase. During Scope, your decision-makers join working sessions to align on goals and sign off on the build plan. During Ship, progress is visible every week – short cycles, no black-box periods. You’re in the room, not waiting for a final reveal. The level of daily involvement is flexible, but the principle is collaboration over handover.

Clean architecture and documented decisions from day one. Every feature goes through Specification-Driven Development (SDD) – defined, reviewed, and approved before implementation begins. The codebase is built so your team can open it, understand it, and extend it. We design for years of growth, upgrades, and pivots, not just a launch milestone.

ROI from AI engineering is measured against specific operational baselines – processing time, error rates, headcount per unit of output, or cycle time. Defining these baselines during the scoping phase is essential. Common measurable outcomes include reductions in manual workflow time (often 50–75%), faster document processing, and shorter time-to-production for engineering teams.

NotchForge™ is the framework behind every Notch engagement. It covers software development, agentic systems, and AI model delivery, from discovery to deployment. One framework, applied consistently across every discipline and every project.

Consistency is hard to maintain without a system. NotchForge™ is the result of years of deliberate practice, built and refined in production. It means every project starts with the same quality expectations, methodology, and governance, regardless of scope, team, or project type.

Three lifecycle tracks:

1. Software Development Lifecycle (SDLC). Software delivery with AI embedded across every role and stage.
2. Agent Development Lifecycle (ADLC). How Notch scopes, builds, governs, and evolves autonomous agentic systems.
3. Model Development Lifecycle (MDLC). AI model work from problem definition and data preparation through training, evaluation, and monitoring.

BDI stands for Business-Data-Interactions. It’s the step before any technical specification. Before writing a Software Design Document or Technical Design Document, we align on the business value, the data that supports it, and the interactions between users, agents, and systems. It’s written in plain language and structured for AI processing in all later stages. It makes sure technical work is grounded in business intent from day one.

Once BDI is complete, we build the Curated Harness for the engagement. For software development, that means a tailored agent swarm setup. For business autonomy, it means agents are configured to perform specific business tasks. We use Sovera AI™ as the accelerator. The setup reflects the specifics of your project, not a generic template.

Accountability stays with people, on both sides. NotchForge™ runs on adapted agile delivery frameworks designed to match the pace of building in the AI world. Engineers make the decisions that matter. Clients stay in the loop. Creativity and judgment are human. The framework is what keeps it all coordinated.

Four things you can count on:

Predictability. The same standard applies to every project. Methodology and quality expectations are clear before the engagement starts.
Transparency. Quality assurance is documented, not assumed. Easier to evaluate, faster to approve.
Compliance. Both are scoped from the start, with rigor matched to the engagement.
Consistency. Standards persist across teams. They’re built into the system, not dependent on any individual.

Yes. The methodology holds regardless of what’s being built or how the project is structured. That’s the point. The governance layer is always on.

It is the process of connecting your systems and applications to Okta Customer Identity Cloud for secure identity and access management.

We provide ongoing maintenance, troubleshooting, and updates to ensure continued performance and security.

Security is ensured through features like adaptive authentication, MFA, and compliance with industry standards.

Deliverables include configured Okta CIC, integration documentation, user training, and ongoing support.

We assess your requirements, configure Okta CIC, integrate with your systems, and provide training and support.

Yes, Okta CIC offers flexible APIs and connectors to integrate with a wide range of platforms, including custom and legacy systems.

It enables personalized experiences, faster onboarding, and secure access for users across platforms.

Benefits include simplified user onboarding, robust security features like MFA, and seamless integration with existing systems.

Product Discovery is a process to identify and validate product ideas that meet user needs and business goals.

Through continuous user feedback and validation during the discovery process.

Yes, it can be combined with technology discovery, UX/UI design, and development services.

Deliverables include user personas, journey maps, prototypes, and a validated product roadmap.

Typically, it lasts from a few weeks to a couple of months depending on scope.

Stakeholders, product managers, designers, and developers should be involved.

Key steps include user research, ideation, prototyping, and validation.

It helps reduce risks by validating ideas early and aligning product features with user needs.

A PoC is a small-scale project designed to test the feasibility and potential of an idea or technology before full-scale development.

We select technologies based on project needs, scalability, and compatibility with your existing stack.

Yes, successful PoCs can be further developed into full-featured products or solutions.

Success is measured by technical feasibility, user feedback, and alignment with business goals.

Deliverables include a working prototype, technical documentation, and a summary of findings and recommendations.

Most PoCs are completed within a few weeks, depending on complexity and requirements.

We define objectives, develop a prototype or demo, test with users, and gather feedback for next steps.

It helps validate technical and business assumptions, reducing risk and informing investment decisions.

Requirements Consulting is the process of gathering, analyzing, and documenting business and technical requirements to ensure project success.

We use change management processes and agile methodologies to adapt and document evolving requirements.

Yes, we cover both business functionality and technical constraints or integrations.

By validating requirements with stakeholders and translating them into unambiguous, testable statements.

Deliverables include detailed requirements documents, user stories, process diagrams, and acceptance criteria.

Key stakeholders, end users, business analysts, and technical leads should participate to ensure all perspectives are covered.

We use structured sessions with stakeholders, employing interviews, workshops, and documentation reviews to capture all needs.

Clear requirements reduce project risks, prevent misunderstandings, and ensure that the final solution meets business needs.

Scrum Coaching provides guidance and support to teams adopting Scrum, helping them understand and implement agile principles effectively.

Success is measured by improved team velocity, quality, stakeholder satisfaction, and achievement of sprint goals.

Engagements range from a few weeks to several months, depending on team needs and goals.

Yes, we provide assessments and recommendations on agile suitability for your projects.

We assess your team’s maturity and challenges, then customize coaching sessions and workshops accordingly.

Our coaches are certified Scrum professionals with extensive experience in agile transformations across industries.

Scrum teams, product owners, Scrum Masters, and relevant stakeholders benefit most from coaching.

It accelerates agile adoption, improves team collaboration, and increases project transparency and delivery speed.

An agentic orchestration platform is the infrastructure layer that coordinates multiple AI agents working together across tools, data sources, and systems. It governs how agents plan, execute, and recover, defining what each agent can do, what it can access, and when a human needs to be involved. Without it, agents operate in isolation. With it, they operate as a governed, coordinated workforce. Sovera AI™ is Notch’s implementation of that layer, built and proved in production before any client engagement.

Traditional AI platforms process inputs and return outputs. An agentic orchestration platform coordinates agents that plan, decide, act, and recover across multi-step workflows. The difference is not capability; it is autonomy and governance. An agentic orchestration platform defines which actions run automatically, which require human approval, and which always need a human decision. It evaluates reasoning quality, not just output accuracy. And it treats deployment as the start of active monitoring, not the end of development.

Data sovereignty in a production-grade agentic orchestration platform is an architectural decision, not a configuration option. Sovera AI™ deploys on-premises, in a private cloud, or fully offline. Data stays in the environment by default. Frontier models receive only what they need to complete a specific task, on a strict need-to-know basis. External access requires explicit authorization. For regulated EU enterprises operating under GDPR, this is the only acceptable default.

Production agentic systems encounter failures. A well-built orchestration platform detects issues in real time, applies recovery logic automatically, and escalates to a human only when the system has exhausted its own recovery options. In Sovera AI™, this is governed orchestration. Tasks move through a structured execution lifecycle: plan, execute, assess, recover, aggregate. The recovery path is defined before deployment. It is an architectural decision, not a response to an incident.

A production-grade agentic orchestration platform is model-agnostic by design. Sovera AI™ routes tasks to any model, i.e. open-weight, private, or approved external, including local offline models that never connect to external services. When a better model becomes available, it can be adopted without re-architecting the platform. The infrastructure is built to outlast any individual model decision. No lock-in. No rebuild required.

Team Extension is a model where external experts are integrated into your in-house team to fill skill gaps and accelerate project delivery.

We handle offboarding, knowledge transfer, and ensure a smooth transition for your team.

Performance is managed through regular check-ins, clear KPIs, and feedback loops between teams.

We focus on onboarding, clear communication, and aligning external experts with your processes and culture.

Models include dedicated teams, project-based teams, co-sourcing, and individual freelancers.

When you need to scale quickly, require niche expertise, or want to augment your team for a specific project.

Benefits include access to specialized skills, flexibility, faster delivery, and cost-effectiveness.

We identify your needs, source and vet talent, onboard them into your workflow, and ensure seamless collaboration.

Technology Discovery is the process of identifying the best technologies to support your business goals.

Recommendations are delivered in a detailed report and presentation.

Usually a few weeks depending on project complexity.

Yes, it is tailored to fit both startups and large enterprises.

We evaluate based on business needs, scalability, cost, and technical fit.

Our senior technology consultants lead the process.

You can expect a technology roadmap, feasibility analysis, and recommendations.

Technology Discovery focuses on technical feasibility and options, while Product Discovery focuses on user needs and product-market fit.

User Research involves gathering insights about user needs, behaviors, and pain points to inform product design and development.

Research findings directly inform design decisions, feature prioritization, and usability improvements.

Yes, we offer both in-person and remote research options, including online interviews and surveys.

We translate insights into clear design or product recommendations and prioritize them with stakeholders.

Deliverables include research reports, personas, journey maps, and actionable recommendations.

Target users, potential customers, and stakeholders relevant to the product or service.

We use interviews, surveys, usability tests, field studies, and analytics review.

It ensures products are built around real user needs, leading to better usability and product-market fit.

UX/UI Design is the process of creating user interfaces and experiences that are intuitive, visually appealing, and effective in meeting user needs.

We use an iterative approach, incorporating stakeholder and user feedback in each design round.

We conduct user research, usability testing, and incorporate feedback throughout the design cycle.

Yes, we offer redesign services to improve usability and visual appeal of current products.

Deliverables include wireframes, interactive prototypes, design specifications, and style guides.

Designers, product owners, developers, and sometimes end users collaborate throughout the process.

Our process includes research, wireframing, prototyping, user testing, and iterative refinement.

It enhances usability, increases user satisfaction, and can lead to higher engagement and retention.