Forward-Deployed ML Engineering
Senior ML engineers. In your repo. Shipping in weeks.
We embed a named engineer into your GitHub, CI/CD, and standup, ship a production agent in 2-6 weeks, and leave you with the eval harness, the runbook, and full IP ownership. Your stack. Your code. No platform lock-in.
50-100x
feature velocity on owned work
7 days
to first PR or pause billing
400K+
production emails sent on a system we built
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.
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
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.
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
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