Agentic systems, applied machine learning and the platform underneath them — designed from day one for the evaluation, governance and unit economics that decide whether a model ever reaches a customer.
Service lines
07
Accelerators
04 reusable assets
Typical entry
Four weeks
You keep
Code, docs and runbooks
Frontier models are a commodity input. Any competitor can rent the same weights you can. The durable advantage sits in the layer nobody demos: how your proprietary data is retrieved and permissioned, how outputs are evaluated before a human trusts them, how failure is caught and rolled back, and what a correct answer costs you at scale.
That layer is an engineering problem, not a procurement one. It is also where most enterprise AI programs quietly stall — a compelling prototype meets a security review, a regulator, a latency budget or a cost-per-query nobody modeled, and the program becomes a slide.
We build from the constraint inward. Evaluation harness before feature work. Retrieval and access control before orchestration. Cost and latency budgets as design inputs rather than post-launch surprises. The prototype is designed to become the production system, not to be thrown away when it meets reality.
Anyone can rent the middle. The rings are the work.
Named, scoped and independently buyable. Most engagements combine two or three.
Multi-step agents that execute real work inside your systems of record — with the guardrails that make that safe.
Turning fragmented internal knowledge into a retrieval layer that respects who is allowed to see what.
Forecasting, classification, risk scoring and optimization where a smaller purpose-built model beats a general one.
The measurement discipline that lets a regulated business put a probabilistic system in front of a customer.
The shared substrate so your fifth AI use case costs a fraction of your first.
Making data an asset the organization can act on — the precondition for everything else on this page.
A design stance, not a service line: no capability we build should be hostage to one vendor's roadmap.
Reusable engineering assets we bring into engagements — reference architectures, tested component libraries and assessment methods. They are yours to keep and modify; nothing here is a runtime you have to keep licensing.
Reference implementation
Golden-set management, LLM-as-judge scaffolding, human review queues and CI regression gates.
Reference architecture
Provider-agnostic routing with failover, caching, budget enforcement and per-team spend attribution.
Component library
Ingestion, chunking and hybrid retrieval that carries your source-system entitlements end to end.
Observability pattern
Structured tracing for multi-step agents — replay any run, attribute any cost, explain any decision.
Four weeks · fixed scope · fixed price
A short, fixed-scope engagement that replaces speculation with evidence. We map candidate use cases against your data reality, build a working thin-slice of the highest-value one, and put a number on what it costs to run.
What you receive
05 outputs
We are deliberately fluent across the credible options in each layer so that the choice can be made on your constraints rather than on ours.
Models
Serving & orchestration
Data & retrieval
Operations
Most engagements start with a short, fixed-scope diagnostic — three to four weeks, a written recommendation, and a costed path forward. If we are not the right firm for it, we will say so and point you somewhere better.