Summarize & draft
Turn long threads, calls, documents or records into a clean summary, or draft the first version of an email, report or reply for a human to approve.
Language models,
built into the product.
Beyond a chat box: LLM features woven into the software your team and customers already use. We build summarization, extraction, drafting and classification that run reliably in production, with the prompts, evaluations and fallbacks that keep them dependable at scale.
In-productfeatures inside your
app, not bolted on
app, not bolted on
Structuredreliable output your
code can trust
code can trust
Evaluatedquality measured
before it ships
before it ships
Structured output
Evaluated before launch
Cost & latency tuned
Model-agnostic
What we build
Features, not
Features, not
a novelty demo.
The useful work language models do inside a product is rarely a conversation. It is the quiet feature that saves someone twenty minutes, every time.
Extract & structure
Pull fields, entities and structured data out of unstructured text, invoices, forms, contracts, into clean records your systems can use.
Classify & route
Tag, triage and route incoming work, tickets, leads, messages, so the right item reaches the right place without manual sorting.
Assist in the workflow
Inline suggestions, rewrites and answers right where the work happens, so people get help without leaving the screen they are on.
What’s included
What separates a feature
What separates a feature
from a science project.
A prompt in a weekend hack is easy. A language feature that behaves the same on the ten-thousandth call is engineering. This is the part we build.
01
Right model for the job
Model selection
We pick and combine models against your quality, cost and latency needs, and keep it swappable, so you are never locked to one vendor.
BenchmarkingCost / latencyRoutingModel-agnostic
02
Prompts as software
Prompt engineering
Versioned, tested prompts and templates treated like code, not magic strings buried in a file, so behavior is intentional and repeatable.
VersioningTemplatesFew-shotGuard prompts
03
Output your code can trust
Structured output
Schema-validated JSON and typed responses with retries on malformed output, so a language model can safely feed the rest of your system.
JSON schemaValidationRetriesType safety
04
Grounded when it matters
Retrieval & context
When a feature needs your data to be right, we ground it with retrieval so it works from your facts, not a training-set guess.
RAGContext windowsGroundingCitations
05
Measure before you trust
Evaluation harness
Test sets and automated scoring for accuracy, format and safety, run on every change, so you ship on evidence, not a good demo day.
Eval setsRegression testsScoringHuman review
06
Behaves in production
Reliability & observability
Streaming, caching, rate handling, graceful fallbacks and tracing of every call, so the feature stays fast, affordable and debuggable live.
StreamingCachingFallbacksTracing
How we work
From a promising idea
From a promising idea
to a feature you ship.
We move from prototype to production deliberately, closing the gap where most AI features stall, the jump from “works in the demo” to “works every time.”
Frame the use case
We define the exact job, what good output looks like and where a human stays in the loop, before writing a single prompt.
Prototype fast
We build a working slice against real examples from your data, so everyone can judge quality on the actual task instead of a slide.
Build the eval
We turn examples into a scored test set, so improvements are measured and regressions are caught before your users find them.
Harden it
We add structured output, validation, fallbacks and guardrails, and tune model, cost and latency until it is ready for real traffic.
Integrate
We wire the feature into your product and workflows, so it feels native to the app rather than an obvious AI add-on.
Launch & monitor
We ship, trace live calls and watch quality and cost, then refine prompts and models as usage and the models themselves evolve.
Direct answers
Questions before
Questions before
you build.
Language features are easy to prototype and hard to make dependable, here are the questions that decide which you get.
Ask us somethingWhichever fits the job. We benchmark options on your task against quality, cost and latency, and keep the choice swappable so you can move as models improve or pricing changes.
Structured output with schema validation, retries on malformed responses, guardrails and an evaluation harness that scores every change. When accuracy against your data matters, we ground the feature with retrieval.
Yes. We build these features into your current application and data through clean APIs, so they sit inside the software your team already runs rather than beside it.
It depends on how many features, the accuracy bar and integration depth. After a short discovery we give a range tied to milestones, plus the ongoing model and infrastructure costs to run it in production.
Contact
Let’s make the next
Let’s make the next
move count.
Tell us what you are building. We will come back within one business day with questions, not a pitch deck.