Skip to main content

Custom AI development

Build the capability around the work, not around the model.

Werkon builds custom AI systems when a distinct operating workflow needs more than configuration or a standalone model call. The work includes the ordinary software, data, rules, permissions, interface, evaluation, and operating controls that make model behavior usable.

Development contract

Own the complete path from source data to operating action.

The model is only one component. A production boundary also defines what the system knows, which tools it can use, how outputs are checked, who may act, what becomes visible when it fails, and how the capability can change safely. If the workflow still needs definition, AI consulting establishes the decision and evidence; AI integration addresses connections to existing systems.

Inputs

Intended workflow and authority
Users, steps, decisions, inputs, outputs, exceptions, affected people, permitted actions, approvals, overrides, escalation, and the consequences of wrong or unavailable behavior.
Authoritative data and knowledge
Source systems, provenance, quality, sensitivity, permissions, retention, retrieval needs, update paths, missing information, and who owns each business fact.
Existing system landscape
Applications, APIs, identity, queues, interfaces, infrastructure, logs, deployment paths, provider constraints, support model, and technology that should remain in place.
Evaluation and operating constraints
Representative cases, quality thresholds, blocked behavior, latency, cost drivers, security, privacy, accessibility, monitoring, incidents, recovery, and handover expectations.

Outputs

Owned system boundary
A documented architecture for sources, software, retrieval, models, tools, permissions, interfaces, review, logging, infrastructure, and the parts controlled by third parties.
Evaluated capability
A working path tested against representative ordinary, difficult, missing-data, adversarial, misuse, and failure cases with results, limits, and release criteria recorded.
Operating controls
Least-privilege access, validation, approval, audit, monitoring, cost controls, incident response, provider-change review, rollback, fallback, and retirement responsibilities.
Client-owned handover
Usable source history, client-controlled accounts where agreed, portable business data, deployment and recovery context, evaluation assets, documentation, decisions, and operating knowledge.

Development path

Build one complete operating path before expanding the surface area.

A thin end-to-end slice exposes the real integration, permission, interface, evaluation, and operating questions earlier than a large disconnected model experiment.

  1. 01

    Define intended use and limits

    Specify the workflow, users, decision, permitted actions, non-goals, affected people, authority, success, stop conditions, and contexts the system must not enter.

  2. 02

    Design the responsibility boundary

    Allocate source authority, deterministic rules, retrieval, model judgment, tools, permissions, review, logging, interfaces, infrastructure, and human decisions.

  3. 03

    Build a thin complete slice

    Connect representative sources to one usable interface and one bounded outcome with real validation, access, failure handling, observability, and safe fallback.

  4. 04

    Evaluate and release deliberately

    Test quality, limits, security, privacy, permissions, user behavior, misuse, cost, latency, failures, recovery, and change impact before an authorized owner accepts release.

  5. 05

    Operate, learn and hand over

    Monitor real use, drift, incidents, overrides, provider changes, cost, adoption, and outcomes while keeping evaluation assets, documentation, access, recovery, and ownership current.

Responsibility allocation

Give each kind of behavior to the component that can carry it safely.

A custom system should not ask a model to perform stable logic or ask a person to compensate for missing controls. The allocation depends on the workflow and can change as evidence improves.

01When the behavior must be exact

Deterministic rules

Use ordinary software for validation, calculations, access checks, state changes, routing rules, limits, identifiers, reconciliation, and other behavior with a known correct form.

Evidence: Explicit rule, test cases, source authority, error behavior, owner, and a controlled change path.

02When the answer depends on approved sources

Retrieval and grounding

Find permission-aware records, passages, facts, or examples and preserve citations, freshness, source boundaries, missing information, and update responsibility.

Evidence: Source coverage, permissions, retrieval quality, provenance, stale-data behavior, citation checks, and fallback when support is missing.

03When bounded uncertainty adds value

Model judgment

Use a model for classification, extraction, ranking, language, vision, prediction, or generation where representative evaluation shows that uncertainty is useful and manageable.

Evidence: Evaluation set, quality by relevant segment, limits, uncertainty, misuse cases, cost, latency, drift, and release threshold.

04When context or consequence requires authority

Human decision

Keep approval, override, professional judgment, exception resolution, release, payment, pricing, hiring, safety, and other consequential decisions with the right authorized person.

Evidence: Named role, usable context, review interface, escalation, audit trail, response path, and authority to reject or stop the system.

Development boundaries

Custom code does not remove model or provider risk.

Ownership improves control only when source, data, accounts, permissions, evaluations, deployment, monitoring, incidents, and third-party dependencies remain visible and usable.

Least privilege reaches tools
The system receives only the data and actions needed for the current task. Tool calls are validated, scoped, logged, and separated from model-generated arguments where consequences require it.
Data and provenance stay explicit
Training, tuning, retrieval, evaluation, prompts, outputs, feedback, and logs each need defined sources, rights, sensitivity, retention, access, and update responsibility.
Release requires evidence
A demonstration does not establish production fitness. Versioned evaluations, security checks, workflow tests, acceptance, monitoring, rollback, and known limitations support release decisions.
Change and incidents have owners
Provider, model, prompt, retrieval, data, tool, dependency, and policy changes can alter behavior. Review triggers, incident response, recovery, notification, and retirement stay assigned.

Engagement fit

Use custom development when the operating responsibility is genuinely distinct.

Good reason to begin

  • A bounded workflow has passed opportunity review and cannot be served well enough by process change, configuration, integration, or an existing product alone.
  • The capability must combine company-specific data, rules, tools, permissions, interfaces, review, and operating controls.
  • The organization can supply representative users, source context, decision owners, evaluation evidence, and an accountable operating owner.
  • Client ownership, portability, system integration, and controlled evolution matter beyond a standalone model or vendor interface.

Resolve before beginning

  • The intended workflow, user, action, authority, or useful outcome is still too broad to evaluate or scope.
  • No representative data, source owner, system context, affected user, risk owner, or acceptance authority can participate.
  • The request assumes model training or a named provider without testing a smaller, safer, or more maintainable alternative.
  • The project requires a regulated opinion, certification, safety authority, or specialist review that has not been secured.

Source basis

Sources behind the control model.

  • 01

    National Institute of Standards and Technology

    Secure Software Development Practices for Generative AI

    NIST SP 800-218A extends secure software development practices for producers of AI models, producers of AI systems, and acquirers across the software lifecycle.

  • 02

    National Institute of Standards and Technology

    Generative Artificial Intelligence Profile

    Cross-sector guidance covering governance, testing before deployment, provenance, incident disclosure, and context-sensitive oversight.

[ WORKFLOW / SYSTEMS AUDIT ]
THE FIRST ENGAGEMENT

Start with one real workflow

A Systems Audit is the usual starting point. If the opportunity is already clear, we can move directly into a focused build.

Show Us the WorkflowStart with the free automation readiness checklist

OBSERVEQUANTIFYDECIDEBUILD

Continue with the work

Choose the next useful path.

Stay with the operating question. The technology can wait until the work is clear.

01

Explore AI services

Compare custom development with consulting, automation, integration, model, language, vision, deployment, and support responsibilities.

Open path02

Build an AI opportunity roadmap

Establish whether a custom capability is supported before committing to delivery.

Open path03

Start with a systems audit

Use a wider operating audit when the AI opportunity sits inside a fragmented system landscape.

Open path04

See AI in a real RFQ workflow

See how a Swiss HVAC manufacturer uses AI extraction, product matching and human approval to process RFQs.

Open path