Quality you can test
Build evaluations around real tasks, difficult inputs and known failures. Check what improves and catch regressions before release.
From AI prototype
To production
We help engineering teams turn AI prototypes into reliable production systems. Tested against real work. Connected to your software. Built to operate.
A promising demo is the start. We take on the engineering that makes it ready for real users: quality, permissions, failure handling and cost. Work directly with the person building it.
A prototype becomes a production system
Follow one AI workflow through evaluation, integration, deployment and daily operation.
Read the film transcript
Prototype: A support assistant answers a question using a sample document. The idea works; its production behavior is still untested.
Evaluate: Test correct answers, missing sources and restricted data. A failed access check is fixed and the evaluation suite is rerun before proceeding.
Integrate: Connect approved knowledge and the support system. Enforce permissions and route uncertain requests to a person.
Deploy: Verify release checks, enable a limited rollout and increase traffic after review. Keep the previous version ready for rollback.
Operate: Monitor quality, latency and cost per task. Investigate a timeout, use the fallback and add the case to the next evaluation run.
From AI prototype to production. Built to work. Ready to operate. Blacksquare Labs.
The engineering behind dependable AI
Your prototype shows what is possible. Production asks harder questions. Does it handle unfamiliar inputs? Respect access rules? Recover when a tool fails? Stay within budget?
We work through those questions in your existing stack, with clear acceptance criteria and an implementation your team can understand and maintain.
Connected to your systems
Connect your data, APIs and workflows with the right permissions, validation and human approval where it matters.
Ready for daily use
Deploy with monitoring, recovery paths and cost controls. Give your team visibility into what runs, what fails and what needs attention.
Define what ready means
Production readiness depends on the work your AI does. We agree the quality bar, operating limits and release conditions before implementation begins.
01 / QualityReal tasks, reference answers and failure casesTest before release
02 / BoundariesData access, permissions and human approvalControl every action
03 / PerformanceResponse time and cost per completed taskSet operating limits
04 / OperationsMonitoring, fallbacks and a rollback planKnow what happens
For teams ready to ship
A prototype with a path to users
You have proved the idea. You need hands-on engineering to close the gap between a working demo and a release your team can support.
AI inside a real product
You are adding an AI feature to existing software and need it to work with your users, permissions, data and release process.
A workflow worth automating
You know the task, the exceptions and the people who own it. You need a dependable way to connect AI to the tools your team uses.
One useful problem, clear boundaries
We start with a defined task and accessible data. If existing software or a simpler integration solves it, that is what we recommend.
One AI workflow
A clear path to live
Start with a production readiness review. We inspect the prototype, data and integrations, identify the release blockers, then scope the engineering needed to get it live.
Take the next release from uncertain to testable
A focused engagement around one workflow. You work directly with the engineer reviewing the system, making the changes and preparing the handover.
- Understand the starting pointReview the prototype, its intended users and the conditions it needs to handle.
- Close the release blockersBuild the evaluations, integrations and recovery paths the workflow needs.
- Release with evidenceTest against agreed criteria, roll out in stages and give your team the controls to operate it.
What you get
- A readiness assessment
- A prioritized view of the gaps in quality, security, integration and operations, with a concrete implementation scope.
- A tested implementation
- The agreed changes, a repeatable evaluation set and a release process with explicit checks and a rollback path.
- A system your team can operate
- Source code, documentation, monitoring and a practical walkthrough. Your team understands the decisions and knows what to do when something fails.
Scope, fees, timeline and acceptance criteria are agreed before work begins. Quality, latency and cost are assessed against the needs of your workflow.
And after launch?
Use production feedback to improve the system. Ongoing support can cover evaluation updates, incident investigation, model changes and cost tuning, with clear ownership and an agreed scope.
From first review to live operation
Each stage answers a practical question: what should this do, how do we know it works, and how will your team run it?
01
Review the prototype
Trace the workflow, inspect the data and integrations, and identify the gaps between today’s demo and the intended release.
Assess
02
Build the evaluation set
Turn real tasks and failure cases into repeatable checks. Agree the quality, latency and cost targets.
Evaluate
03
Engineer the production path
Connect the systems, enforce access rules, validate outputs and add recovery or human review where needed.
Integrate
04
Deploy in controlled stages
Verify the release criteria, start with limited traffic and expand with monitoring and a rollback plan in place.
Deploy
05
Operate and improve
Review failures, response time and cost in use. Feed what you learn back into evaluations and the next release.
Operate
Production AI expertise for your client projects
You lead the product and client relationship. We bring hands-on AI engineering to the delivery: evaluations, integration, deployment and handover. Work under your brand or with us as a named technical partner, with responsibilities agreed from the start.
Tell us about your client’s projectDiscuss your project
What stands between your AI and production?
Tell us what you are building, what works already and where you need help. We’ll reply by email to discuss the project and a useful next step.
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Frequently asked questions
Can you work with our existing prototype?
Yes. We start by reviewing its code, data, integrations and intended use. We retain what works and scope the changes needed for the next release.
Which models and platforms do you work with?
We choose around your existing stack, data requirements and operating constraints. Model providers, hosting and tools are confirmed during the technical review.
What does production-ready mean for our project?
It means meeting the acceptance criteria agreed for your workflow: task quality, data permissions, failure handling, performance, cost and operational ownership. Readiness is supported by test results and a release plan.
Will our team own the code and infrastructure?
You receive the project source code, documentation and a handover. We agree deployment access and ownership upfront, including any third-party services or licenses the system depends on.
Is the film a live client project?
The film is an illustrative workflow with a simulated interface. It explains the engineering process; it does not present client results or performance benchmarks.
How much does an engagement cost?
We start with a fit conversation, then agree the scope and fee for a readiness review or implementation. You receive a written proposal before paid work begins. Ongoing support is scoped separately.