How is this different from hiring a consulting firm?

Three things that live in the MSA, not the pitch deck. The engineer you meet in the scoping call is the engineer writing the PRs, and their name is in the SOW. No platform license attached, we pick your stack and sign away IP in writing. First PR in 7 days or billing pauses, week 2 prototype with a pass or fail rubric and a refund and exit gate. Partner plus grads firms structurally cannot do any of those three.

Who owns the IP and the repo?

You do, from clause one. No proprietary agent framework, no platform license, no vendor attached runtime, no data lock in. Your repo, your weights layer, your eval harness, your runbook, yours in writing before the engineer starts week 1.

What does an engagement cost?

Fixed weekly rate, published up front, scoped to a specific outcome. Most engagements run 4 to 8 weeks and price like hiring one staff level MLE plus a tech lead at cost, without the 58 day hiring cycle, the $400K permanent liability, or the ramp. You get a fixed fee in writing at the end of the free scoping call.

What happens after you leave?

Three things land in your repo at handoff: the runbook (what to do when it breaks), the eval harness (how to tell if a change made it worse), and the architecture doc (why it was built this way). The engineer who ran the engagement is available for paid 2 hour consults at a capped rate for 12 months. No retainer, no surprise bills.

Forward-Deployed ML Engineering

Senior ML engineers. In your repo. Production in 6 weeks.

We embed a named engineer into your GitHub, CI/CD, and standup, ship a production agent in 2 to 6 weeks, and leave you with the eval harness, the runbook, and full IP ownership. Your stack. Your code. No platform license. No vendor attached runtime.

Book a scoping call See case studies

60 minutes with a senior engineer. No pitch deck. You leave with a written one pager.

50-100x

feature velocity on owned work

7 days

to first PR or pause billing

400K+

production emails sent on a system we built

Shipped to production for

Monetizy.ai8K emails/day, week 1
Upstate Remedial Management400K+ emails sent
OpenLawCursor for Lawyers
PriceFoxshared retrieval agent
OpenArtcreative agent surface

What makes us different

Three things no direct peer combines

Engineers in your repo on day one

Named senior ML engineers write PRs inside your GitHub, use your CI/CD, and hand off a working eval harness and runbook when the engagement ends. Not a partner-plus-grads consulting stack. Not a license-attached FDE team. The code is yours.

Platform-agnostic, IP stays with you

We pick the right model and orchestration stack for each problem and sign away IP in writing. No Distillery, no Writer agent framework, no Glean lock-in. You own the repo, the weights layer, and the 12-month-out runbook.

Week-2 prototype, week-6 production, or pause billing

Specific milestones you can hold us to. 50-100x measurable throughput on the work we own. The counter to the MIT NANDA stat your boardroom quotes: yes, 95% of pilots fail, and here is the rubric for not being one of them.

Build vs buy vs forward deploy

The four options your board is weighing

Every VP of Data and AI we talk to is choosing between the same four doors. Here is what each one actually costs when the agent has to be in production.

	Hire in house	Big 4 consulting	AI platform vendor	fde10x
Time to first shipped PR	58 day hiring cycle, 6 to 12 week ramp	RFP, 8 week kickoff, partner plus grads	License plus 3 to 6 month integration	7 days or we pause billing
Who writes the code	Your hire, once you find them	Rotating team, the senior you met is not on the PR	Their implementation partner, at their margin	Named senior engineer from the scoping call, on the SOW
IP, repo, eval harness	Yours, with the permanent liability	Mixed per MSA, often joint	Platform attached, you lease the runtime	Yours from clause one, in writing, before week 1
Model and cloud choice	Your call	Usually the partner cloud (Microsoft, AWS)	Platform locked	Anthropic, OpenAI, Bedrock, Vertex, Azure, or open weight
Exit cost	$400K+ fully loaded, permanent	Retainer, hard to stop cleanly	Re platform in 12 to 18 months if you leave	Week 6 handoff, runbook in your repo, zero lock in
Risk gate	90 day probation, then sunk cost	None, change order culture	Annual contract	Week 2 rubric with refund and exit clause

The in house number assumes one staff level MLE at US market comp plus benefits, ramp, and management overhead. Big 4 assumptions are based on published government contract disclosures.

Shipped systems, not decks

Running in production, cited on the record

Named clients with shipped outcomes. No screenshot-only demos. No "case studies coming soon."

Monetizy.ai

AI email campaign orchestrator, ~8K emails/day, delivered in 1 week

LLM orchestration, retrieval, deliverability

Upstate Remedial Management

AI email client for auto-debts notice handling, 400K+ emails sent to date

Agentic email workflow, compliance checks, audit trail

OpenLaw

AI-native law editor ("Cursor for Lawyers"), publicly released

LLM product surface, legal-domain retrieval, eval harness

PriceFox

Shared retrieval agent at industry-leading performance via automated ML engineering pipeline

Multi-tenant retrieval, automated eval, research writeup

All case studies →

The engagement rubric

Week-by-week, not quarter-by-quarter

Week 0

Scoping call

60 minutes with a senior engineer. We scope one specific production outcome, not a 40-page roadmap.

Week 1

Engineer onboarded

Named engineer in your GitHub, Slack, and standup. First PR in 7 days or we pause billing.

Week 2

Prototype in your staging

Running on your stack, hitting your data, behind your feature flag.

Week 6

Production + handoff

Eval harness, runbook, and CI/CD in your repo. Your team owns the system from here.

Full engagement model →

Protocol-native

MCP-native. A2A-compatible. Runs on your Bedrock, Vertex, or Azure.

Procurement now filters on protocol specificity. We speak MCP (10,000+ servers, 97M monthly SDK downloads) and A2A (v1.0 with 150+ orgs including Microsoft, AWS, Salesforce, SAP, ServiceNow).

The agent we ship runs where your security review already cleared. Not in a SaaS control plane you have to re-legal.

# Integrations you do not have to re-negotiate

model_provider: bedrock | vertex | azure_openai | anthropic

orchestration: langgraph | pydantic_ai | custom

retrieval: pgvector | turbopuffer | pinecone | your_own

protocol: mcp + a2a

eval: ragas + custom_rubric + human_review

ci: your_github_actions

deploy: your_infra

# What we explicitly do not bring

- no platform license

- no proprietary agent framework

- no vendor-attached runtime

The first four questions procurement asks

Before you book the call

Full FAQ for procurement →How the engagement runs →

Your board asked when the agent ships.
Let us put a date on it.

60-minute scoping call with a senior engineer. We leave you with a one-pager: the outcome we would own, the milestone rubric, and a named engineer to deliver it.

Book the call

Free. 60 minutes. Senior engineer, not a sales rep.