The hard problems that make enterprise AI actually work.
A demo is easy. A system that is correct, grounded, safe, and affordable in production is a research problem. We work evaluation, agent reliability, retrieval, post-training, and safety — and fold every result straight back into what we build.
This isn't research for its own sake, and it isn't frontier model-training for a lab. It's applied research — the specific, unglamorous problems that decide whether an enterprise AI system holds up on real data, under real load, in front of real regulators.
We treat every build as an experiment with a hypothesis and a metric. The techniques below are how we close the gap between a system that demos well and one you can put your name on.
Principles
- Measure first — no eval, no ship
- Grounded over generative; refusal is a feature
- A human on every consequential decision
- Reproducible pipelines you own and can re-run
Every build is an experiment.
The same loop, on every engagement: a hypothesis tied to a business metric, the smallest system that tests it, and a gate that proves it before it ships.
Where we go deep.
Eight problems we work so your systems don't fall over in the long tail — each one connected to where it ships across the stack.
Evaluation & benchmarking
You can't ship what you can't measure. The hard part of enterprise AI isn't a demo — it's proving, continuously, that a system is correct, safe, and better than the baseline.
Our approach. We build task-specific eval suites and benchmarks tied to your risk surface — factuality, citation coverage, task success, safety, and bias — and run them in CI so regressions surface before production.
Agent reliability
Agents that plan and call tools fail in long-tail ways — wrong tool, malformed arguments, no recovery, silent errors. Reliability, not raw capability, is what blocks production.
Our approach. We design bounded scopes, typed tool interfaces, and verifiers that check each step, with retries, stop conditions, and shadow-mode evaluation of task success before any autonomy is granted.
Retrieval & grounding
Retrieval-augmented systems hallucinate when retrieval is weak or context is stale. Grounding quality is what decides whether an answer can be trusted.
Our approach. We tune hybrid lexical-plus-semantic retrieval, rerankers, chunking, and freshness, and measure citation coverage and factuality — with refusal and handoff when confidence is low.
Model selection & routing
The best model for a task changes constantly, and the frontier isn't always the right answer — cost and latency matter. Choosing and routing across models is an engineering problem.
Our approach. We benchmark candidate models on your actual tasks, route each request to the cheapest model that clears the quality bar, and cache or distill where it pays off.
Fine-tuning & post-training
Sometimes retrieval isn't enough — a base model needs your domain, your format, or your judgment. Post-training is powerful and easy to get wrong.
Our approach. Where it's genuinely warranted, we curate data and apply supervised fine-tuning and preference optimization (RLHF / DPO), with held-out evals to prove the gain is real and not memorized.
Safety, alignment & governance
Consequential AI has to refuse the right things, resist adversarial input, and treat people fairly — and prove all of it to a regulator.
Our approach. We design refusal and escalation behavior, run adversarial and jailbreak testing, evaluate bias and fairness on your decisions, and produce audit trails and model-risk documentation.
Data & synthetic generation
Good evals and good models need good data — and real data is scarce, sensitive, or unlabeled. Generating and curating it is a discipline of its own.
Our approach. We curate and label domain data and generate synthetic data for evaluation and training where privacy or coverage demands it, validated against real distributions.
Multimodal & document AI
Enterprise data isn't clean text — it's scanned PDFs, tables, photos, and mixed formats that break naive pipelines.
Our approach. We combine vision-language models, OCR, and schema-constrained extraction to turn messy documents into structured, provenance-tracked data your systems can rely on.
If it isn't measured, it isn't shipped.
Every system is tied to an evaluation harness that runs in CI — quality, safety, fairness, and cost tracked continuously, with regressions caught before production. We build the benchmark from your real cases, keep a held-out split the system never trains on, and report the delta against your current baseline.
The techniques in our toolkit.
We reach for whatever the problem needs — and tell you honestly when a simpler approach wins.
Every result ships — and compounds.
There's no research shelf here. An eval we build for one client becomes a reusable harness; a retrieval trick that lifts factuality becomes a default; a verifier that catches an agent failure gets folded into the platform. The work compounds — which is why the second system we build with you starts further ahead than the first.
Bring us a hypothesis. Leave with a system.
Tell us what's eating your team's time. We'll give you an honest read on whether AI is the right tool — and if it is, a scoped v1 with a timeline and cost.