For fintech, regtech, and insurtech engineering teams
Govern AI coding at the workflow level.
See the payload before it is sent, control which provider and model handle the call, version the instructions that shape the work, and observe usage and cost for every request.
Provider approval is only part of the problem when each developer still operates a separate AI workflow.
The current assessment starts through a scoped contact request. No marketing opt-in is required to receive the follow-up.
Call 0241 / pre-send
payment-service / risk review
Payload
- Files
- 7 selected
- Context
- 18.4k tokens
- Excluded
- 2 files
Workflow
- Skill
- risk-review@v3
- Owner
- platform
- Status
- reviewed
Provider route
BYOK / approved model
Outcome
model + latency + tools
Cost
visible per call
Approved provider, invisible workflow
Approval does not make a fragmented workflow governable.
Security may know which vendor is allowed while Engineering still cannot reconstruct how developers use it in practice.
- 01
Payloads cannot be inspected
Code, files, repository memory, and chat history can leave the editor without a reviewable pre-send view.
- 02
Accounts and keys are fragmented
Individual subscriptions, provider accounts, and credentials make routing policy difficult to operate as a team.
- 03
Prompts and rules stay private
Architecture guidance and delivery standards live in personal sessions instead of versioned, reviewable workflows.
- 04
Usage cannot be reconstructed
A seat count does not show which model ran, what it received, how many tokens it used, or what the call cost.
Visibility before send
Put human oversight where the decision still matters.
Context Lens exposes the assembled context before a model call. Developers can inspect what is relevant, remove what is not, and understand the token footprint before data leaves the workflow.
Human oversight and data minimization become normal engineering actions, not a late audit of an event the team can no longer reconstruct.
Read the Context Lens guideContext Lens / payload review
Files and memory selected for this call
Human oversight
Review and adjust before send.
Data minimization
Exclude context that is not needed.
One reviewable operating layer
A connected workflow from context to operational evidence.
Knotic makes the handoffs between payload, instructions, provider, and outcome explicit. The provider remains important; it is no longer the only control the team can see.
- 01Inspect
Inspect context
Review the assembled payload and remove irrelevant context before a call leaves the workflow.
Reviewable payload
- 02Apply
Apply versioned instructions
Use repo-native Skills as Code so engineering rules have owners, history, and review.
Shared operating rules
- 03Route
Route provider and model
Make provider, model, and credential path explicit for the job instead of leaving routing implicit.
Controlled execution
- 04Observe
Observe outcome and cost
Connect each call to model, tokens, latency, tools, files, and estimated cost for team review.
Operational evidence
AI Coding Governance Assessment
Find the workflow gaps that matter first.
The assessment maps current AI coding operations across four dimensions. It produces an engineering maturity score and a prioritized gap view, not a compliance score.
Start the assessmentCurrent delivery: a scoped conversation through the Knotic contact form. Assessment follow-up is separate from any marketing consent.
- 01
Context visibility
Can the team inspect and minimize the payload before it reaches a model?
- 02
Provider governance
Are provider, model, account, and key decisions explicit and reviewable?
- 03
Shared workflows
Are reusable prompts and engineering instructions versioned with clear ownership?
- 04
Usage and cost visibility
Can leaders reconstruct per-call usage and distinguish useful spend from workflow waste?
What a 30-day pilot tests
Test the operating model on a bounded workflow.
A focused pilot tests whether workflow-level visibility is useful in daily engineering work before a broader rollout.
Discuss a 30-day pilot- 01
Repository
One production-relevant codebase with a bounded workflow.
- 02
Team
5-10 developers across engineering and platform roles.
- 03
Skills as Code
3-5 versioned instructions for repeatable team workflows.
- 04
Baseline
Provider, model, payload, usage, and cost signals captured from day one.
- 05
Review
A final review of adoption, operating gaps, and rollout recommendations.
Evaluation resources
Give Engineering and Security the same system view.
FAQ
Questions from Engineering and Security.
Can we use BYOK?
Yes. Knotic supports bring-your-own-key workflows across supported cloud providers. Provider and model choices remain explicit inside the shared operating layer rather than disappearing into individual developer accounts.
Can we use local models?
Yes. Teams can point Knotic to a local OpenAI-compatible endpoint and configure local models. The right route can be selected for the workflow instead of forcing every task through the same provider.
Does governance slow developers down?
The goal is to place visibility and reusable controls inside the coding workflow. Developers review context where they work, while shared instructions and provider routes reduce repeated setup and private re-prompting.
Is this a compliance certification?
No. The assessment is an engineering maturity review, not legal advice, a certification, or a compliance score. It identifies operating gaps that Engineering and Security can evaluate together.
What does the assessment produce?
You receive a maturity score across four workflow dimensions, a prioritized view of the most material gaps, and a practical recommendation for a focused pilot or next operating step.
Start with the workflow you have today
Make AI coding visible before you try to govern it.
Map current context, provider, workflow, and usage visibility. Leave with a maturity score and prioritized next steps.
Take the AI Coding Governance Assessment