Which models do you work with?

We pick the model that earns the slot — Claude for long-context reasoning, GPT-4-class for fast loops, open-source for cost-sensitive paths. We don't tie the ship to one vendor.

How do you avoid prompt spaghetti?

A typed prompt library, versioned in Git, with evals in CI. Every change ships with a metric, not a vibes-based approval.

What about data privacy?

PII scrubbing at the edge, retention controls, and per-tenant isolation where it matters. We work with your security team to write the policy that survives a SOC 2.

Do you do the AI features yourself?

We design and ship the first version, then hand it over with the eval set and the runbooks. The team that owns the feature keeps ownership after we leave.

Service · Intelligence

AI & Decision SystemsCopilots that actually ship.

From retrieval pipelines to agentic workflows — we design, evaluate, and integrate AI features that move metrics, not just screenshots.

Scope a ai track See the capabilities

42%less time per decision
11enterprise customers live
$3.20median cost per decision
99.2%eval-set stability

Track metadataAI & Decision Systems

Senior lead in the room First slice in week one

Headline metric42%less time per decision

You leave with

Eval harness
Prompt library
Agent runbooks

LLM product featuresRAG & knowledge graphsAgent workflows & toolsEval, guardrails, observability

Capabilities

Six things we do unusually well.

Every capability below is something we have shipped at least three times. No inflated menus, no services we‘d outsource to a stranger. Pick one, pick three, or pull the whole list into a Studio track.

LLM product features

Chat, summarise, classify, extract — designed as a real product surface with provenance, undo, and an export path back to your data.

RAG pipelines

Chunking, embedding, and retrieval strategies that survive real documents. Eval-driven, with a confidence band and source citations on every answer.

Agent workflows

Tool-using agents that actually finish the task — with budgets, retries, and a human-in-the-loop at the points that matter.

Eval harness

A versioned eval set in CI. Every prompt change ships with a metric that proves it improved something real, not just vibes.

Guardrails

PII scrubbing, prompt-injection defence, and rate limits wired into the same observability stack your engineers already use.

Observability

Per-request traces, token spend, latency, and the cost-per-decision your finance team will ask about on day one.

The five-step rhythm

How a ai track actually runs.

We work in visible slices with a rhythm you can feel. Senior hands on the work, demos on the calendar, and a written log of every bet we make and how it lands.

Step 01Frame

We pick the one decision the AI should help with, the metric that proves it's helping, and the audit log that proves it's safe.

01 / 5

Step 02Prototype

A working eval harness in week one, the prompt in week two, and the first real-user test by week three.

02 / 5

Step 03Harden

Guardrails, observability, and the cost model your finance team will ask for. Wired into CI, not a notebook.

03 / 5

Step 04Ship

Cohort rollout behind a flag, with a kill switch and a clear path back to the previous behaviour if the metrics slip.

04 / 5

Step 05Compound

We leave the eval set, the prompt library, and the agent runbooks with the team that owns the feature after we go.

05 / 5

Proof in numbers

Beautiful motion is great.
Beautiful outcomes are better.

A few of the headlines from the engagements behind this track. The boring details (logs, audit trails, the Loom explaining why) ship with every engagement.

42%less time per decision

11enterprise customers live

$3.20median cost per decision

99.2%eval-set stability

What you leave with