AI features your users actually reach for
We build real applications on large language models: grounded in your data, evaluated for quality, and shipped into your product as a feature that does a real job.
The right one, turned into the light
Commercial
When quality leads
The strongest available model for the task, when the job is hard and the answer has to be right.
Open-weight
When cost or control leads
An open model where the economics or the ownership matter more than the last increment of quality.
Private
When nothing may leave
A private deployment for sensitive work, so your data never leaves your control.
AI Integration & Development
Anyone can prompt a model. Shipping one is the hard part.
A prompt in a playground is a party trick. A product is what happens when that capability is grounded in your data, guarded against failure, measured for quality, and built into the flow your users are already in. That's the hard part, and it's the part we do.
We've shipped LLM features into real products, and we build them like features instead of experiments.
Engineered to be trusted as well as impressive
Built for your workflow
An application shaped around the job your users are actually doing, never a generic wrapper.
Grounded in your data
It answers from your content and context, with retrieval and guardrails keeping it off the open web's guesses.
Model-agnostic
We pick the right model for the task and can swap it as the field moves, so you're never locked in.
The shapes an LLM feature can take
Copilots & assistants
In-app assistants that draft, summarize, and act inside the tool your team already uses.
Content & generation
Generate copy, replies, reports, and structured output on demand, in your voice and format.
Semantic search & Q&A
Ask questions across your documents and data and get cited, grounded answers back.
Structured extraction
Turn messy input into clean, typed data your systems can act on automatically.
Workflows & agents
Multi-step features that plan, call your tools, and complete a task end to end.
Classification & routing
Categorize, tag, and route inputs so the right thing happens without a human sorting first.
A feature you can stand behind
The difference between a demo and a feature is everything on this list. Grounding decides whether an answer is defensible, evaluation decides whether a change made it better or only different, and the fallbacks decide what your users see on the day the model is wrong.
- 01Grounded with retrieval (RAG) so answers cite your real data
- 02Evaluated against a test set before and after every change
- 03Guardrails and fallbacks so it fails safe, never silently wrong
- 04Cost and latency controlled, with the right model for each call
- 05Instrumented so you can measure the lift after it ships
- 06Shipped into your existing codebase, stack, and UI
Value in days, instead of a quarter of hope
Discover
We pin down the job, the users, and what "good" looks like, in terms you can measure.
Prototype
A working slice in days, tested on real inputs so you feel it before you fund it.
Ship
Built into your product with evals, guardrails, and monitoring from day one.
Measure
We track the outcome and tune, so the feature earns its place.
Recent work in this space
Wherever the work happens
Inside your SaaS
An AI feature your users love (drafting, search, or automation) that lifts activation and retention.
Internal tools
A copilot for your team that turns hours of manual work into a review-and-approve.
Customer-facing
Assistants and generators that help customers self-serve and convert, on your own domain.
Operations
Classification, extraction, and routing that quietly remove a whole tier of manual handling.
From prototype to a feature you can measure
Prototype on real inputs
A working slice is tested against your actual data instead of a synthetic demo, so you can judge it early.
Evals & guardrails added
A test set, retrieval grounding, and fallbacks get built in before the feature earns production traffic.
Shipped to production
The feature goes live inside your product, instrumented so its impact is measurable from day one.
Tuned against real usage
The feature is refined against what real users actually do with it, never what the demo assumed.
What actually changes once it ships
Answers cite where they came from
Retrieval grounding means a claim traces back to your real data instead of a plausible-sounding guess.
Value in days, ahead of any roadmap slot
A working prototype on real inputs lands fast enough to judge before you commit further budget.
The right model for each call
Cost and latency stay controlled because the model is chosen per task instead of defaulting to the biggest one.
Fails safe, never silently wrong
Guardrails and fallbacks mean an uncertain answer defers instead of confidently inventing one.
Never locked to one model
Model-agnostic architecture means switching providers is a config change instead of a rebuild.
Impact you can actually measure
Instrumentation from day one tells you whether the feature earned its place.
A lens on its own is a loupe
Have a feature your users wish existed?
A wrapper sends a prompt and hopes. We build the instrument around it (grounded, evaluated, guarded, and measured), then ship it into the product your users are already in.
What you get
6 things handed over
The evaluation suite is what separates this from a prototype. Without it, every later change is a guess about whether the feature got better, and the answer only arrives through your users, which is the most expensive place to find out.
- 01A working AI feature shipped into your product
- 02Retrieval over your data with citations
- 03An evaluation suite to catch regressions
- 04Guardrails, fallbacks, and cost controls
- 05Monitoring and analytics on real usage
- 06Clean, documented code your team can own
Built by people who ship features instead of experiments
We ship applications, not demos
Every feature is grounded, evaluated, and guarded before it earns production traffic. It's engineered like a real feature, never left as a prompt in a playground.
We're model-agnostic by design
Commercial, open-weight, or privately deployed: we pick the model that fits the task and can swap it as the field moves.
We ground answers in your data
Retrieval and citations mean the feature answers from what's actually true about your business, never the open web's guesses.
We evaluate before and after every change
A test set catches regressions, so a change that looks fine in a demo doesn't quietly break in production.
We build in guardrails instead of hope
Fallbacks and constrained outputs mean the feature fails safe instead of confidently inventing an answer.
We instrument from day one
Monitoring and analytics tell you whether the feature earned its place, beyond the fact that it shipped.
A partner that tests before it commits you
We test on your real inputs first
A working prototype runs against your actual data within days, so you judge it before committing further budget.
We hand over code your team owns
Clean, documented code means the feature doesn't become a black box only we understand.
We keep you off model lock-in
Switching providers as pricing or capability changes is a config change instead of a rebuild.
We measure impact as well as uptime
Instrumentation tracks whether the feature actually moves the metric it was built for.
We keep tuning after launch
Models and data change. We stay on to tune the feature against real usage, long after the day-one demo.
We're honest when a wrapper would do
If the job doesn't need a custom application, we'll say so instead of overbuilding it.
What teams ask before they build
Isn't this just a wrapper around a chatbot?
No. A wrapper sends a prompt and hopes. We build an application: grounded in your data, evaluated for quality, guarded against failure, and integrated into your product and systems, engineered like any other feature you'd ship.
Which model do you use?
Whichever fits the task. We're model-agnostic across commercial and open-weight models, and we design so you can switch as pricing and capability change. We'll recommend based on quality, cost, and privacy needs.
How do you keep it from making things up?
We ground answers in your real data with retrieval, constrain outputs, add guardrails and fallbacks, and evaluate against a test set. Where it isn't confident, it says so or defers instead of inventing.
Can it use our data privately?
Yes. We build with your data handled securely and scoped, and for sensitive use cases we can use private deployments or open models so nothing leaves your control.
How fast can we ship something?
A working prototype on your real inputs usually takes days, so you can judge value early. A production feature follows, hardened with evals and monitoring.
What happens after launch?
AI features need tending, because models and data change. We hand over clean, documented code, and can keep tuning and monitoring on a managed plan if you'd like.
More in AI Integration & Development
Let's talk
Running a large platform, shaping a first MVP, or getting a product ready for a funding round? Tell us where you are. We'll shape the process around it, and stay with you after launch.
