The last mile
Most AI projects stall between the model and the production system. The FDE owns that distance.
AI Deployed · Embedded
Embed with your team. Architect, build, run.
AI and software systems, in your environment, under your governance.
Embedded engineers
The role that closes the gap. Between the demo and the production system.
A Forward Deployed Engineer is a senior engineer embedded with your team — owning the deployment, translating between the AI system and the operations it serves.
Embedded, not external
They sit inside your stack, tools, and review queues. They ship where the work is.
Accountable in production
They carry the pager, run the rollout, and own the outcome against the business — not the demo.
What they do
In your environment, against your systems.
- DeployStand the agent up in your environment, against your systems, behind your approval queue.
- IntegrateWire it into the tools your team already uses.
- ObserveInstrument the runtime, watch the outputs, catch regressions early.
- GovernTune the approval mode, the guardrails, the data discipline.
- Hand offWhen the system is steady, transfer operation to a named owner on your team.
Embedded. Accountable. On-call.
Engagement
How an embedded engagement runs.
Six phases, one card. Hit play to walk through the engagement — diagnose, embed, ship, hand off, follow up, re-engage.
Phase 01 of 06
Diagnose
25%
Shadow the team. Map the work. Write the diagnosis.
The FDE joins your standup, shadows the team for one sprint, and writes a one-page diagnosis of the actual bottleneck.
Diagnosisv0.3
Work queue · 3 items
- Diagnosis one-pagerIn review
- System mapPending review
- Engagement scopeApproved
What we bring
What an embedded engagement delivers.
Our people
Senior engineers, embedded
Named senior engineers on every engagement — the same people on the call are the people doing the work.
Diagnosis before code
The FDE shadows the team for a sprint, writes a one-page diagnosis, and only then opens a PR.
Review and handoff
Every PR is reviewed by your team. The engagement ends when your team runs the work without the FDE.
Quietly opinionated
Strong views on the modern stack and the right amount of AI — we bring them and adjust when your team pushes back.
Our stack
Web and APIs
TypeScript end-to-end, Next.js, Go, Python. Postgres, Redis, queues, search. On the platforms your team already uses.
Data plumbing
Merge legacy sources into one queryable warehouse. Fix the schema. Ship the migration. Hand off the runbook.
AI where it helps
Embeddings for search, LLMs for triage, agents for the queue. We bring the configuration, guardrails, and audit log.
Internal tools that ship
The lightweight admin tool the team needs but never built. Live in two weeks, in your repo, behind your auth.
Use cases
Built for one job. Built for your environment.
Six functions. Each agent scoped to the work, governed by your rules, reviewed by your team.
Support
Triage inbound. Draft replies for your team to approve.
Sales
Personalized follow-ups after every demo. Matched to your voice.
Operations
Reorder low-stock SKUs, route approvals, keep the ops inbox clean.
Analytics
Weekly pipeline summaries, risk callouts, board-ready notes.
Finance
Reconcile invoices, flag anomalies, draft approvals for the controller.
Marketing
Newsletter drafts, subject-line tests, social scheduling.
Differentiators
What makes us different.
01
Custom build, not a generic assistant.
Each agent is scoped to one job — a goal, a tool allowlist, guardrails, and an approval mode. We design the configuration with you, against your systems.
02
An approval gate you control.
Every agent operates under an approval queue. Drafts land there first. The agent never sends, writes, or triggers directly. We cannot bypass it from our side.
03
We architect, build, run.
Your team reviews the queue. We handle the runtime, monitoring, configuration drift, and model updates. If the agent does not perform, we change the configuration, the model, or the policy.
Engagement
From discovery to an agent in production.
Four steps. Running in production by week four. After that, we run the agent and tune the configuration as your business changes.
- Step 01
Discovery
We learn the work, your systems, and your governance requirements.
First - Step 02
Architect and build
We design the agent — goal, tools, guardrails, approval mode — and build it against your systems.
Build - Step 03
Review
Your team reviews the drafts. We tune the configuration from what your team edits.
Review - Step 04
Run and improve
We operate the agent, tune the model, and update the policy as your business changes.
Operate
FIG · 01Deployment capability map8 nodes
Frameworks for sequencing AI initiatives. Outcome-defined, time-bound, tied to operational reality. — 30 / 60 / 90 day milestones
Translates strategy into an executable plan: AI systems, integration, governance, rollout. — 1 → N target state
Pipelines, products, and the systems that feed AI. Warehouses, lakes, and the layers between. — T+0 decision latency
Risk, compliance, observability, and accountability for AI in production environments. — 100% audit coverage
Engineers deploy and integrate solutions in production — applications, data, operations. — 6–18 mo avg. engagement
Connects existing systems — applications, data, processes — into one operational fabric. — API · event contract patterns
Operate, monitor, and continuously improve deployed systems with structured service delivery. — 24 / 7 operational SLA
Iterate based on feedback, expand adoption, optimize performance as needs grow. — 47+ deployments benchmarked
Deployment capability map
Commitments
The operational guarantees.
Approval gate
Every agent has an approval queue. Nothing leaves without your team.
Full audit
Two synced logs. Every input, output, and decision — append-only, queryable.
Data discipline
Sensitive fields stripped before the model. PII never reaches the model.
Start
Let's talk.
A short conversation. We'll tell you when we're not the right fit.