Services
AI & Data
Turn data into decisions with applied AI, LLM products, and analytics that ship.
We build AI and data systems that drive decisions in production, not demos that stall in review — from LLM-powered products to the pipelines and models underneath them. Our engineers own the full path: data engineering, model selection and evaluation, retrieval and fine-tuning, and the production plumbing that keeps outputs accurate, observable, and affordable. You receive working software running in your own stack, with accuracy and cost-per-request figures you can defend to a board.
Why it matters
Most AI efforts stall in the gap between a promising prototype and a system people trust in production, because evaluation, data quality, and cost control were treated as afterthoughts. Setting the accuracy baseline and unit economics early is what decides whether AI compounds into an advantage or drains budget as a permanent experiment.
What we do
Inside AI & Data
The specific work we take on — each engagement scoped to what your product actually needs.
LLM and RAG product engineering
We design retrieval-augmented systems with chunking, embedding, and reranking tuned to your specific corpus, then wrap them in guardrails and structured outputs. Latency, context limits, and hallucination rates are treated as engineering constraints with tests attached, not left to hopeful defaults.
Data engineering and pipelines
We build batch and streaming pipelines that land clean, versioned data in your warehouse or lakehouse with full lineage and enforced contracts. Schema drift, late-arriving records, and idempotent reprocessing are handled up front so downstream models and reports stay correct.
Machine learning and forecasting
We train and validate models for prediction, classification, ranking, and demand forecasting, choosing the simplest method that clears the target metric. Every model ships with a baseline comparison, a holdout evaluation, and a defined trigger for retraining as data shifts.
Model evaluation and guardrails
We build evaluation harnesses that score accuracy, groundedness, and regressions against real examples before anything reaches users. Prompt-injection defenses, output validation, and fallback paths keep behavior predictable when inputs are malformed or adversarial.
MLOps and deployment
We put models behind versioned APIs with monitoring for drift, latency, quality, and cost per request in production. Canary rollouts and reproducible training runs mean a bad model is caught and rolled back on evidence rather than guesswork.
Analytics and decision tooling
We turn raw events into governed metrics, a semantic layer, and dashboards that answer the questions operators actually ask. Definitions are versioned and shared, so two teams reading the same number reach the same conclusion.
How we work
Our approach, by effort
Where a typical engagement's time actually goes — front-loaded on getting it right, not just building fast.
- Frame & evaluate20%
- Data & retrieval30%
- Build & guardrail30%
- Deploy & monitor20%
- 01
Frame & evaluate
20%We set the accuracy baseline and cost target, and build an evaluation harness before shipping anything.
- 02
Data & retrieval
30%Pipelines, chunking, embeddings, and reranking tuned to your corpus, with lineage and enforced contracts.
- 03
Build & guardrail
30%LLM and ML services with structured outputs, prompt-injection defenses, and fallback paths.
- 04
Deploy & monitor
20%Versioned APIs with drift, latency, quality, and cost-per-request monitoring, plus canary rollouts.
Use cases
Where this shows up
The shapes ai & data work most often takes when teams bring us in.
RAG assistant
Ground an LLM in your own docs with retrieval, guardrails, and tested accuracy.
Agent workflow
Automate multi-step tasks with tool-using agents that stay observable and safe.
Forecasting model
Predict demand, churn, or risk with the simplest model that clears the target metric.
Analytics layer
Turn raw events into governed metrics and dashboards teams actually trust.
What you get
- A production LLM or ML service deployed in your environment, with source code, tests, and API documentation
- Data pipelines with lineage, data contracts, and monitoring wired into your warehouse or lakehouse
- An evaluation harness and benchmark set that scores model quality automatically on every change
- A model card documenting training data, metrics, known limitations, and cost per request
- A governed metrics layer and dashboards with agreed, versioned definitions
- An operations runbook covering monitoring, retraining triggers, and incident response
Our toolkit
Tools & technologies
The stack we reach for on ai & data engagements — chosen for how it behaves in production, not how it demos.
Is this you?
When teams bring us in for ai & data
- You have a promising AI prototype that keeps stalling before production
- You need an LLM feature (RAG, agents, assistants) that's accurate and cost-controlled
- Reporting is slow or contradictory and decisions wait on the data team
- You want AI grounded in your own data, with evaluation and guardrails, not a demo
FAQ
AI & Data, answered
The questions teams ask us most before an engagement.
How do you keep LLM outputs accurate and safe?
We build evaluation harnesses that score accuracy and groundedness on real examples before anything reaches users, plus prompt-injection defenses, output validation, and fallback paths. Quality is a tested engineering constraint, not a hope.
Which models do you use — do we have to use OpenAI?
No. We pick models on accuracy, latency, and cost for your task, across OpenAI, Anthropic, open-source, and self-hosted, and design so you can swap providers without a rewrite.
How do you control AI running costs?
We measure cost per request from day one, cache and route where it helps, and right-size models per task. You get accuracy and unit-economics figures you can defend to a board.
Is our data used to train external models?
No. We architect for privacy — your data stays in your environment, and we use providers and configurations that don't train on your inputs.
Have a project in mind?
Tell us where you’re headed. We’ll tell you the fastest, soundest way to get there.