AI-Powered Solutions
Intelligent systems that learn, predict, and automate your business processes
Calling a language model API is four lines of code. Running one in production is a different exercise, because a model call is unlike any other dependency: it is slow, it is priced per request in a way that scales with success, it fails in ways that return a success status, and its output is not deterministic.
We build AI features that hold up after launch — with validated outputs, enforced authorization, per-user cost controls, and an evaluation suite that catches quality regressions before your customers do. The demo is the easy part; we build for the part that comes after.
What's Included
Key Benefits
Retrieval Quality Is the Whole Product
When an AI assistant gives a bad answer, the instinct is to blame the model and change the prompt. In practice the failure is upstream almost every time: the right passage was never retrieved, so the model was answering from nothing.
We build retrieval properly — chunking on document structure rather than character counts, hybrid search combining semantic and keyword matching so exact tokens like error codes and product names are found, reranking the candidates, and rewriting follow-up questions into standalone queries so multi-turn conversation actually works. These changes typically matter more than which model you use.
Treating Model Output as Untrusted Input
Model output is not a return value. It is a suggestion from a system optimized to sound correct, and it must pass validation before it touches anything that matters. We use structured output modes and validate the parsed result against a schema anyway.
Where output drives an action — sending an email, updating a record, calling another service — validation includes whether the action is permitted, not merely whether it parses. A model prompted to be helpful will confidently propose operations the current user has no right to perform, so authorization is checked in code against the real user, every time.
Cost Control as an Engineering Discipline
AI costs scale with usage in a way most infrastructure does not, and the surprising invoice usually arrives immediately after the feature succeeds. We structure prompts so the stable portion comes first and can be cached by the provider, route routine requests to smaller models and reserve the expensive one for genuinely hard reasoning, constrain output length, and trim retrieved context to what actually improves answers.
Then we instrument it — token counts and computed cost logged per request and attributed to an account. Without attribution you cannot tell whether a rising bill is broad growth or one user stuck in a loop, and those require completely different responses.
Refusal, Citations, and Evaluation
The most damaging behavior in a production AI feature is a confident answer assembled from irrelevant context. Users cannot distinguish it from a correct one, and a single instance destroys trust in the whole system. We make refusal a first-class path, with relevance thresholds and a useful destination when the system does not know.
Every answer carries citations, which makes it verifiable for users, testable for you, and measurably reduces fabrication. And we build a regression suite of real questions with known-good answers that runs on every prompt and model change, because AI features degrade silently and nothing in a normal test suite will notice.
How We Work
- 1
Use case validation
We assess whether the problem genuinely needs a model, and what the cost and accuracy targets need to be for it to make sense.
- 2
Data & retrieval foundation
Source ingestion, chunking, embedding, and hybrid retrieval — evaluated in isolation before any generation prompt is written.
- 3
Generation & guardrails
Grounded prompting with citations, schema-validated outputs, authorization on any action, and an explicit refusal path.
- 4
Evaluation harness
A regression suite scoring retrieval and generation separately, so quality changes can be traced to a cause.
- 5
Cost & latency tuning
Prompt caching, model routing, streaming, and per-user rate limits sized against your unit economics.
Frequently Asked Questions
Will our data be used to train someone else's model?
Not under the enterprise and API terms we build on. Major providers exclude API data from training by default, and we confirm retention and training policies in writing as part of the design. Where requirements are stricter, we can architect around self-hosted open models so nothing leaves your infrastructure.
How do you stop the AI from making things up?
Several controls together. Answers are grounded strictly in retrieved context, every claim carries a citation, retrieval results below a relevance threshold are treated as no result, and the system is built to say it does not know rather than improvise. No approach eliminates the risk entirely, which is why consequential outputs also get a verification step or a human in the loop.
What does it cost to run per month?
It depends on volume and how much context each request carries, and it varies by orders of magnitude between designs. We model it during discovery using your expected request volume, then build in prompt caching, model routing, and output constraints — which routinely reduce a naive implementation's cost by most of its total.
Which model should we use?
Usually more than one. Routine classification and extraction run well on smaller, cheaper models, while genuinely hard reasoning justifies a frontier model. We benchmark candidates against your actual tasks rather than public leaderboards, because the ranking on your workload is frequently different.
Can you integrate AI into our existing product?
Yes, and that is the majority of our AI work. We add capabilities to systems that already exist rather than requiring a rebuild, routing model calls through your backend so keys, rate limits, cost attribution, and provider portability stay under your control.