How do we get an AI system into production and keep it there?
Production AI systems
Sold asEnterprise AI Platform & Agentic Workflows · Fixed-scope architecture plus production build
A demonstration has one happy path, a clean input and somebody watching. A production system has every input your business can generate, nobody watching, and a consequence attached to being wrong. We build the second kind: internal copilots, workflow agents, voice agents and the middleware underneath them, designed around who is allowed to see what, what happens when a model fails, what gets logged, and where a human signs off. That is the part that decides whether it is still running in a year.
The gap between a demo and a system
Closing that gap is most of the engineering. It means deciding what happens when the model is unavailable, when the input is malformed, when the output fails validation, when a downstream system rejects a write, and when someone needs to know six months later why a particular decision was made.
What the design actually covers
Data access and permissions
What the system can read and write, acting on behalf of whom, and how that is enforced rather than assumed. Who can see the model's own prompts, and where they are processed, is a hosting question, and our guide to where a private LLM should run, listed at the end of this page, compares the routes.
Failure modes
What happens on timeout, malformed output, rate limit, partial write, and downstream rejection, each one designed rather than discovered.
Evaluation
Regression suites that run when prompts or models change, so a quality drop is caught before users find it.
Human review
Where a person sits in the loop, what they are shown, and how their corrections feed back.
Auditability
A record of what ran, on which inputs, producing which output: the thing that makes the system answerable afterwards. It is also the evidence an AI governance framework relies on to show that a policy clause is enforced.
Safe by default
The engineering defaults matter more than the model choice: idempotent operations so a retry cannot double-write, dry-run modes so a change can be inspected before it lands, audit logs as a by-product rather than an afterthought, and kill switches that work.
None of them shows in a demo, and together they decide whether automation is safe to depend on. Where the automation touches money or a regulated process, agentic workflow design adds deterministic gates that decide what proceeds.
Where a workflow already runs on a no-code platform and is straining against this list, our guide to when Zapier is not enough, listed at the end of this page, sets out the signs and what to try before rebuilding.
Each of those defaults also carries a running cost, from the regression runs to the logs someone has to read, and our guide to estimating AI ROI shows where they belong in a business case next to the build.
A buyer can write defaults like these into the contract as well as the design. Our AI statement of work template shows where they go: logging and switch-off, human review points, acceptance tests, model changes and handover.
The same defaults decide which service desk tickets and runbook fixes an agent can be trusted with, and our guide to AI for IT operations sets them out in the order to hand the work over.
Where the data is US patient information, the same design also has to meet HIPAA, and our guide to HIPAA-compliant AI sets out the architecture and the vendor agreements that takes.
What this looks like in production
Three figures from systems we run, each one published in full on the engagement file it links to.
- ~1,170
automated tests cover the speaking pipeline on one production estate, among them a regression suite where disabling any one guardrail breaks specific frozen cases.
Our count, in CI · Engagement file D-02
- ~300 to 1,400+
the automated test estate on that same platform, over the course of the engagement.
Our count, in CI · Engagement file D-02
- 60%
lower average cost per session on another client's AI platform after we re-architected its skills estate.
Our measurement on the platform · Engagement file D-01
What you get
- Working system running against production data
- Evaluation and regression suites
- Human review paths and escalation routes
- Audit logging and observability
- Runbook and handover documentation
Start here if
- A prototype works but nobody will let it near production
- An AI feature shipped and quietly degraded
- There is no way to tell whether output quality has changed
- The system needs to write to a system of record
Frequently asked questions
How do you evaluate an AI system that has no single right answer?
By separating the parts that do from the parts that do not. Structure, schema, and factual grounding can be checked deterministically. Genuinely subjective quality is scored against a fixed set of graded examples, so the question becomes whether today's output is worse than last week's rather than whether it is perfect.
What happens when the model provider changes the model underneath us?
This is exactly what the regression suite exists for. Without one, a silent provider-side change is discovered by users. With one, it is caught on the next run and the routing layer can pin or reroute while the difference is assessed.