Key Takeaway
An eval suite built from your real use cases, production monitoring and security controls are what let you ship AI changes with the same confidence as any other release.
The demo went well. Someone on your team wired a model to your data over a couple of sprints, and the CEO showed it to the board. Now there’s a launch date.
Then the first beta users arrive. They paste in a 40-page PDF instead of a paragraph. They ask questions in French. One of them types “ignore your previous instructions” just to see what happens. Your engineer changes a prompt to fix one complaint and quietly breaks two other things nobody notices for a week.
None of that means the prototype was a mistake. A prototype answers one question: can this work at all? Production asks a different set of questions, and most teams discover them one incident at a time.
Why prototypes stall before launch
Prototypes get built under ideal conditions. Clean inputs, a handful of test cases, one user who knows exactly how to phrase things. The engineer who built it checks outputs by reading them.
That approach stops working once usage grows. You can’t read every output. You can’t remember every edge case. And LLM behavior is non-deterministic, so a passing test today can fail tomorrow on the same input.
Most teams also hit a staffing problem. The engineer who built the prototype has a roadmap to get back to. The work left to do is unglamorous: test harnesses, logging, threat modeling, retry logic. It’s real engineering, and it competes with every other priority on the board.
So the prototype sits behind a feature flag for a quarter. Or it ships without guardrails and the team spends the next quarter firefighting.
What we build
We take your working prototype and build the layer around it that makes it safe to put in front of customers. The model and the core logic stay recognizably yours. What changes is everything that tells you whether it’s working.
An evaluation suite built from your real use cases
Evals are the foundation. Without them, every prompt change is a guess.
We start by collecting real inputs: beta logs, support tickets, examples your product team considers “good” and “bad.” From those we build a labeled dataset that covers your common paths and the edge cases that worry you.
Then we write graders. Some are deterministic, like checking that output parses as valid JSON, that a cited document ID exists, or that a refusal happens when it should. Others use a model as a judge, scored against a rubric your team signs off on. Where judgment matters most, we calibrate the model-graded scores against human labels so you know how far to trust them.
The suite runs in CI on every change to prompts, models, retrieval settings or tool definitions. A pull request that drops accuracy on billing questions gets flagged before it merges, the same way a failing unit test would.
This also makes model upgrades boring, which is what you want. When a new model version comes out, you run the suite and compare. You get a number instead of a debate.
Production monitoring and tracing
Traditional APM tells you a request took 4 seconds and returned a 200. It doesn’t tell you the answer was wrong.
We instrument the full request path with tracing: the user input, the retrieved context, every model call, every tool call, token counts and latency at each step. When a customer reports a bad answer, your team can pull the trace and see exactly where it went sideways. Was the retrieval wrong? Did the model ignore good context? Did a tool return an error that got swallowed?
On top of tracing, we set up the metrics that matter for AI features specifically:
- Quality signals, including sampled online evals and user feedback like thumbs down or regenerate clicks
- Cost per request and per customer, so a runaway prompt doesn’t show up first on your invoice
- Latency percentiles broken down by step, so you know whether the model or your retrieval is the bottleneck
- Failure modes such as refusals, empty responses, malformed outputs and timeouts
Alerts go where your team already looks. If you run on Datadog or Grafana, we wire into those rather than adding another dashboard nobody opens.
Security controls
Any feature that passes user input to a model has a new attack surface. Security teams know this, and they will ask.
Prompt injection is the obvious one. A user, or a document the user uploaded, includes instructions meant to override your system prompt. We handle this in layers. Untrusted content gets clearly separated from instructions. Inputs get screened for known injection patterns. Most importantly, the model’s permissions get scoped so that even a successful injection can’t do much damage. If the model can only read data the current user is already allowed to see, a leaked system prompt is an embarrassment instead of a breach.
We also cover data leakage paths. That means PII handling in logs and traces, checks that one customer’s data never appears in another customer’s context, and output filtering where your domain requires it. If you’re sending data to a third-party model provider, we document what goes where so your security review has something concrete to work from.
Architecture for real load
Prototypes usually call the model synchronously and hope. Production needs more care.
We add retries with backoff for provider errors and rate limits. We add fallbacks, such as a secondary model or a graceful degraded response, so an outage at your provider doesn’t become an outage for you. Where response times allow, we move long-running work to background jobs with status updates. We add caching where inputs repeat, and timeouts everywhere.
None of this is exotic. It’s the same discipline you apply to any external dependency. LLM providers just fail in more varied ways than most APIs.
Where humans stay in control
Production-grade doesn’t mean fully autonomous. For most features, the right design keeps a person in the loop at specific points.
We work with your product team to decide where those points are. That might be a review queue for low-confidence outputs, a confirmation step before the feature takes any action on a customer’s behalf, or a kill switch that lets on-call disable the feature without a deploy. The eval suite and monitoring tell you where the model is reliable. Human review covers the rest.
Demo versus production
A quick way to tell which one you have:
| Question | Demo | Production |
|---|---|---|
| How do you know a change helped? | Someone tried a few examples | The eval suite score went up |
| How do you find bad answers? | A customer tells you | Monitoring flags them |
| What happens during a provider outage? | The feature breaks | Fallback kicks in |
| Can security sign off? | Not yet | Yes, with documentation |
If your prototype lands in the left column on most of these, that’s normal. It’s also the work we do.
Your part
We need repo access, the prototype itself and examples of what good output looks like. A product owner who can label edge cases and approve the eval rubric speeds things up. Your engineers don’t need to pause the roadmap.
How the build works
You describe the feature and where it’s stuck. We scope the work with you and agree on a fixed price tied to what gets delivered, before anything starts.
The build itself is done by AI agents supervised by senior engineers. Those engineers own the architecture decisions, review every change and handle the integration with your systems. The output is production-grade code in your repo, following your conventions, with documentation your team can maintain.
Delivery takes days to weeks depending on scope, not months.
Frequently asked questions
Will you rewrite our prototype from scratch?
Usually not. If the core logic works, we keep it and build around it. Where something has to change for production, like moving from a synchronous call to a background job, we explain why before we change it.
Which eval and observability tools do you use?
Whatever fits your stack. Open-source frameworks and commercial platforms both work. If you already pay for something, we use it.
Can our team run the eval suite after you're done?
Yes. It lives in your repo and runs in your CI. Adding a test case is as simple as adding a row to the dataset.
What if we switch model providers later?
That's one of the main reasons to have evals. Swap the provider, run the suite, compare the scores.
