AI Production Readiness
From AI MVP to a production-ready system
An independent audit of your AI-built or AI-assisted MVP - architecture, security, data, evaluation and cost - scored and turned into a prioritized hardening plan, with the option to have Comtau close the gaps.
For founders with an AI-built or AI-assisted MVP, especially ahead of a fundraise, a security review, or a push to scale.
- Senior architect-led
- Architecture-first diagnosis
- Hands-on in code
- AI systems expertise
Powered by Comtau Inc.
The offer
- 01AssessReadiness level across 8 dimensions
- 02PlanRisk-ranked findings and a prioritized hardening plan
- 03HardenOptional: Comtau closes the gaps with your team
A fixed-scope audit that ends in a hardening plan, not a report that sits on a shelf.
Works in the demo, breaks in production
Sound familiar?
AI coding tools make it fast to reach a working demo. They don’t check whether the result is ready for real users, real data or a real security review.
If two or more of these are true, the gap between demo and production is real.
Trust & safety
- Security and auth gapsYou’re not fully sure who can access what.Authentication, authorization and secrets handling were built fast, not reviewed.
- Data integrity issuesSupport tickets mention data that looks wrong or missing.Data flows and storage were assembled quickly, without a data model review.
- A security review you can’t answer yetThe honest answers would be guesses.An investor or customer has asked for a technical or security review.
Quality & visibility
- No evaluation of output qualityQuality depends on spot-checking and gut feel.There’s no structured way to check whether the AI’s output is actually good.
- No observabilityIncidents get found by users first.No tracing, no cost visibility, no error budget on what the system does in production.
Cost & maintainability
- Runaway inference costThe AI bill is a surprise every month.Model and token usage scale with traffic in ways nobody sized in advance.
- Code nobody fully understandsEvery change feels riskier than it should.Large parts of the codebase were generated and merged without a senior review.
What we assess
The 8 readiness dimensions, and how each is rated
Every audit rates the same eight dimensions on the same four levels, so the result is comparable and nothing important is skipped. The level tells you what to fix first - it is not a grade.
- Demo-gradeWorks for the demo path only
- FragileWorks for early users, fails unpredictably
- Production-readySafe to launch and sell
- Scale-readyHolds up as usage and team grow
01Architecture
Whether the system design holds up under real load and change
Checks: Component and data-flow review · Scaling and failure-mode check
- Demo-gradeOne process, no failure handling
- FragileWorks under light load; failure modes unknown
- Production-readyFailure modes known and handled; clear boundaries
- Scale-readyScales by design; changes stay isolated
02Code quality
How maintainable and reviewable the AI-generated code actually is
Checks: Static analysis and hotspot review · AI-authored code audit
- Demo-gradeGenerated code merged unreviewed
- FragilePartly reviewed; few tests
- Production-readyCritical paths reviewed and tested
- Scale-readyThe team can change it safely and quickly
03SecurityAI
Authentication, authorization, secrets, dependencies and prompt/agent safety
Checks: Access-control review · Dependency and secrets scan · Prompt-injection and agent-permission check
- Demo-gradeSecrets in code; broad access
- FragileAuth in place; roles and agent permissions unclear
- Production-readyLeast-privilege access; secrets managed; prompt inputs guarded
- Scale-readyChecks automated; a security review passes
04Data
Data model integrity, storage and handling
Checks: Schema and data-flow review · Data handling and retention check
- Demo-gradeSchema grew ad hoc
- FragileModel works; integrity unchecked
- Production-readyIntegrity enforced; retention defined
- Scale-readyData flows documented and governed
05EvaluationAI
Whether model output quality is measured at all
Checks: Evaluation approach review · Quality-metric recommendations
- Demo-gradeSpot checks by eye
- FragileA sample set checked by hand
- Production-readyAn evaluation set runs before each release
- Scale-readyEvaluation automated; quality tracked over time
06Scaling
What breaks first as usage grows
Checks: Load-path analysis · Capacity headroom estimate
- Demo-gradeLimits unknown
- FragileFirst bottleneck found when it breaks
- Production-readyBottleneck known; headroom measured
- Scale-readyCapacity planned ahead of growth
07Observability
Whether you can see what the system is doing in production
Checks: Tracing and logging review · Cost and error visibility check
- Demo-gradeConsole logs
- FragileErrors logged; no tracing
- Production-readyErrors, latency and traces visible
- Scale-readyAlerts on cost, quality and latency
08CostAI
Inference and infrastructure cost as usage grows
Checks: Unit-economics review · Cost-control recommendations
- Demo-gradeNobody knows the cost per user
- FragileThe monthly bill is tracked
- Production-readyCost per request known; limits set
- Scale-readyUnit economics modelled against growth
What you receive
A readiness score and a plan you can act on
The score exists to drive decisions, not to decorate a slide. Every finding is ranked and paired with what to do about it.
- 01Readiness level per dimensionAll 8 dimensions, rated on the format above.
- 02Risk-ranked findingsEach finding ranked by severity and by the effort to fix it.
- 03Prioritized hardening planNow, next and later - sequenced, not a wish list.
- 04Architecture recommendationsWhat to keep, what to rework, and what not to rebuild.
- 05Investor-readiness notesWhat a technical reviewer would ask, and where the answers stand.
AI Production Readiness Report
Illustrative example · not client material
Readiness score and prioritized hardening plan
Illustrative- Dimensions scored
- 8
- Format
- Fixed-scope audit
- Output
- Score + hardening plan
Readiness score
Each of the 8 dimensions scored against what a production system needs - not against an arbitrary benchmark.
Findings
- high riskAuthentication allows broader access than intended.
- high riskNo structured evaluation of model output quality.
- mediumObservability covers errors but not cost or latency.
- on trackCore data model is sound.
Hardening plan
- nowClose authentication and access-control gaps.
- nowAdd an evaluation harness for model output.
- nextAdd cost and latency observability.
- laterPlan for horizontal scaling once usage grows.
From audit to hardening
How an engagement runs
A fixed-scope audit, a plan, and the option to have Comtau close the gaps. Each stage ends at a gate, so nothing moves on until the previous step is agreed.
- 01
Assess
Score the system across all 8 readiness dimensions.
- Architecture and code review
- Security and data check
- Evaluation and observability review
OutputReadiness score and findings report
Gate: Findings walked through with you
- 02
Plan
Turn findings into a plan you can act on.
- Prioritise by risk and effort
- Sequence quick wins vs structural work
- now
- Blocks launch, or exposes users and data
- next
- Limits scale, quality or cost as usage grows
- later
- Makes the system easier to change
OutputPrioritized hardening plan
Gate: Plan and priorities agreed
- 03
Harden
Close the gaps that matter most.
- Fix critical findings in scoped units
- AI-assisted, human-controlled implementation
- Rebuild weak components where needed
OutputA system that passes its own audit
Gate: Re-checked against the scorecard
- 04
Handover
Leave the team able to keep it that way.
- Document decisions
- Set up ongoing checks
OutputHandover package
Execution options
Start with the audit alone, or have Comtau carry it through to a hardened system.
Audit only
The readiness score and hardening plan, delivered as a report your team executes.
- Assess
- Plan
Audit + hardening
Comtau closes the findings alongside your team, in priority order.
- Assess
- Plan
- Harden
- Handover
Ongoing architecture support
Continued senior oversight as the product scales past its first production release.
- Handover
- ongoing
What “fixed” means when Comtau hardens the system
Each finding is closed as a bounded unit of work, implemented with AI-assisted engineering under human control. A unit is not done until it closes with a record like this one, and the dimension is re-scored.
Verification record
Illustrative example · not client material
Guard tool calls in the support agent behind explicit permissions
Closed- Finding
- Security · agent permissions
- Scope
- Agent tool layer only
- Re-scored
- Fragile → Production-ready
Scope
Tool calls from the agent go through a permission check. Out of scope: prompt templates, retrieval, billing.
Positive checks
- passAllowed tools still work on the evaluation set.
- passExisting test suite unchanged and green.
Negative controls
- passA prompt asking the agent to call an admin tool is refused and logged.
Review
- no blockersIndependent security review of the change.
Open items
- deferredPer-tenant rate limits on tool calls → next unit in the plan.
Controls on every hardening unit
- Bounded units of work
- Every unit states its mission, the parts of the system it may change, what is out of scope and the checks that will prove it.
- Independent review
- Separate read-only reviews check architecture, security and evidence. Whoever did the work does not sign it off.
- No green by shortcut
- Tests are never weakened, skipped or rewritten to make a check pass. An unexplained failure blocks closure.
- People own the irreversible calls
- Architecture changes, new dependencies and any release need explicit human approval.
Ready to see where your AI MVP actually stands?
Tell us what you’ve built and what’s next - a fundraise, a security review, a push to scale. We’ll tell you plainly what a readiness audit would find.
Direct conversation with a senior CTO/architect. We’ll review your situation and determine the useful next step.
Who it is for
Built for founders past the demo stage
Pre-fundraise founders
- Situation
- An AI-built MVP that works in demos, ahead of investor technical questions.
- Where the audit looks first
- Closing the gaps a technical reviewer would find first.
- What you walk away with
- A system - and a story - that holds up in diligence.
Teams facing a security review
- Situation
- A customer or partner is asking for a security review before signing.
- Where the audit looks first
- Authentication, data handling and dependency exposure.
- What you walk away with
- A defensible answer instead of a scramble.
Products about to scale
- Situation
- Usage is about to grow past what the prototype was built for.
- Where the audit looks first
- Architecture, cost and observability.
- What you walk away with
- A system that scales on purpose, not by accident.
Already in a fundraise? See the investor-side view in technical due diligence.
Why Comtau
Judgement first, not a checklist tool
- Senior architect-led
- Every audit is led by a senior architect, not a junior reviewer running a scanner.
- Architecture-first diagnosis
- We look at system design and decisions, not only code-level symptoms.
- Controlled execution
- If you want Comtau to fix the findings, the work is done unit by unit, each re-checked against the scorecard and closed with evidence.
- AI systems depth
- We work across model providers, retrieval and agent architectures - not one vendor’s stack.
- We run it ourselves
- Comtau builds its own software with AI coding agents under these same controls, so the advice comes from operating practice.
FAQ
AI production readiness questions
Do you just report the issues, or fix them too?
Both are available. The audit-only option gives you a scored report and a hardening plan your team executes. The audit + hardening option has Comtau close the findings directly, in priority order.
Is this different from a full rebuild?
Usually, yes. Most AI-built MVPs need targeted hardening, not a rewrite - the audit tells you which parts of the system are sound and which need work, so you don’t rebuild more than necessary.
How long does an audit take?
It’s a fixed-scope engagement, scoped to the size of your system rather than an open-ended review. We’ll give you a concrete timeline once we understand what you’ve built.
Is our code and data safe with you?
Yes. We can start under an NDA if you’d like one in place before sharing access, and access is limited to what the audit needs.
Which AI stacks and tools do you review?
We’re vendor-neutral: model providers, orchestration frameworks, vector stores and agent frameworks are all in scope. The audit looks at your architecture and decisions, not at which brand of tool you chose.
Does this help with investor due diligence?
Yes - a readiness report is one of the strongest things you can bring to a technical due-diligence conversation. If you’re already in a fundraise process, see technical due diligence for the investor-side view.
What happens after the audit?
You get the score and the plan either way. Most teams either have Comtau execute the hardening plan directly, or take it in-house with Comtau available for ongoing architecture support.
Find out what your AI MVP is hiding before it costs you
Tell us what you’ve built. We’ll scope a fixed-price readiness audit and tell you plainly what it would find.
Direct conversation with a senior CTO/architect. We’ll review your situation and determine the useful next step.
What happens next
- 01You describe what you built and why (fundraise, security review, scaling)
- 02We scope a fixed readiness audit
- 03You get a score, findings and a hardening plan
Talk to a senior CTO/architect
Book a call