01
Generative AI applications
Chat, search, and copilots grounded in your data, with the retrieval, guardrails, and eval harness needed to trust the output.
AI & MACHINE LEARNING
AI that works in production, not just in demos.
We build machine learning and generative-AI systems that hold up under real users, real data, and real cost constraints — from first prototype to the boring plumbing that keeps them alive at 3 a.m.
TRUSTED BY TEAMS ACROSS
NORTHWINDACME CORPMERIDIANHELIOSOAKRIDGE
Why do so many AI projects die between the demo and production?
73%
of AI projects never make it to production — most fail at integration, monitoring, or cost, not at the model.
10×
the cost of running a model in production vs. training it. Nobody talks about this in the pitch deck.
6 mo
typical time to a working prototype. Then most teams stall for another year trying to operationalize it.
What we actually deliver
No decks of icons. Three real practices, each led by senior people who ship.
02
Predictive ML systems
Forecasting, scoring, and recommendation systems that learn from your data and stay accurate as the world moves.
03
MLOps and evaluation
The pipelines, monitoring, and eval sets that turn a notebook into something your on-call rotation can actually own.
HOW WE WORK
A short, honest process. Then real work.
01
Frame the problem honestly
Timeline · 1–2 weeks
Half the value is in refusing to build the wrong thing. We stress-test the use case against data, cost, and what a human would do instead.
KEY DELIVERABLES
- Use-case brief with success metric
- Data availability and quality assessment
- Baseline (non-AI) benchmark to beat
02
Prototype against real data
Timeline · 3–8 weeks
We build the smallest end-to-end system on your actual data — not a curated sample — and evaluate it against the baseline before we scale.
KEY DELIVERABLES
- Working prototype with eval harness
- Latency and cost profile at target scale
- Go / no-go recommendation with the numbers
03
Ship, measure, iterate
Timeline · Ongoing
We don't disappear on go-live. We stay close, watch the numbers, and keep making the thing better in the open with your team.
KEY DELIVERABLES
- Production release with rollback plan
- Live metrics tied to business outcomes
- Post-launch iteration cadence
WHAT YOU GET
Outcomes, not features
What actually changes for your team, your customers, and your numbers when this ships.
Production, not slideware
Every engagement targets a live system users touch — not a lab demo that impresses the board and dies.
Cost you can plan around
We size and instrument for token, GPU, and infra cost from week one. No surprise invoices after launch.
Evaluations you trust
Every model change runs against an eval set built with your domain experts. You know when quality drops before your users do.
Human oversight built in
Feedback, override, and audit paths are part of the design — not bolted on when compliance asks.
USE CASES BY INDUSTRY
Where this service actually lives
Financial Services
- Document intake and extraction for lending
- Fraud and anomaly detection in real time
- Client-advisor copilots grounded in your knowledge base
- Portfolio commentary automation
- Call-center summarization and QA
AI & MACHINE LEARNING
Financial Services
Work that speaks for itself
Ask about a similar project →Financial Services82% straight-through processing
Document-intake copilot for a mid-market lender
Replaced a 40-person offshore extraction team with a human-in-the-loop AI pipeline reviewed by five underwriters.
Read Case Study
Retail+19% conversion on search sessions
Grounded search for a specialty retailer
Rebuilt on-site search with retrieval-augmented generation and query understanding — with the eval harness to keep it that way.
Read Case StudyFREQUENTLY ASKED
Questions people actually ask us
Less than you think for generative AI use cases — grounding on your documents can work with hundreds of examples. Predictive models need more, and we'll tell you up front if you don't have enough.