Applied AI · Consulting

AI systems that make it to production.

Agent engineered. Expert approved. You, always in the loop.

deploy · production
01 · Live demo

Ask our engineering team

Most AI engagements end with a slide deck. Ours end with deployed systems — built by a multi-agent engineering team coordinated by an EM agent. We set the architecture, the agents build, and nothing merges without our review. This is a scripted replay of how a real request flows through it. Pick one.

EM agent — online
EMMorning. Backlog is groomed and CI is green. What do you need?
EM agent COORDINATES Backend APIS · DATA Frontend UI · UX DevOps INFRA · CI Security THREAT · AUDIT QA TESTS · EVALS EXPERT REVIEW GATE — NOTHING MERGES WITHOUT IT
idle — awaiting request
02 · Selected work

Systems in the field

Each diagram is the actual shape of the system — the part generic solutions get wrong.

✕ PRODUCE WINDOW DEPOT A DEPOT B DEPOT-AWARE ROUTING · HARD CONSTRAINTS ENCODED
Logistics

Fleet-wide route optimization at national scale

Optimized a Fortune 100 grocery retailer’s entire fleet — supply depots folded into the routing algorithm, with strict produce-handling rules deciding which shipments could combine. Same pattern delivered for a nationally distributed beverage brand.

Read the case →
client.com app. mail. cdn. api. dev-2. legacy. img. ■ ASSETS THE CLIENT DIDN'T KNOW EXISTED EXPLORATION BOUNDED BY AGREED RULES
Security

Attack-surface discovery algorithms

Algorithms that map an organization's external domain structure — routinely surfacing assets clients didn't know they had. Scoring and exploration logic co-designed with client security teams, under strict rules for what could be probed.

Read the case →
DRIFT CAUGHT EVAL SCORE PER MODEL · WEEKLY RUNS CAUGHT BY THE HARNESS, NOT BY A CUSTOMER
Agent systems

Agents and evals that earn trust

Agents that reached logistics workers by text, email, and phone — tuned for people skeptical of bots, with clear disclosure. Plus eval frameworks that track LLM behavior over time, so drift gets caught before customers see it.

Read the case →
03 · Approach

How engagements run

Start from the constraint

Produce spoilage windows, exploration boundaries, workers who hate bots — the messy rule that breaks generic solutions is where the design starts, not where it gets patched.

Measure before believing

Scientific training runs deep: claims get instrumented. Evals, baselines, and monitoring are built with the system, so you know what improved and by how much.

Ship, then hand over

The engagement ends with a deployed system and a team that can run it — documentation, review process, and operational playbook included. No permanent dependency on the consultant.

04 · Start

Have a system that needs to ship?

Your domain expertise ⊕ our engineering team. A 30-minute call is enough to tell whether this is a fit — bring the problem, no prep needed.

Not sure yet? Start with the Systems Review — a fixed-scope, two-week diagnostic.

Book a call

Founded by scientists turned AI leaders — national-lab research backgrounds, then senior data science and AI roles shipping production systems across logistics, security, and adtech.