| Target Entities | Forward Deployment Engineering, Forward Deployment Engineer, AI Deployment Pod, AI in Production, Agent Readiness, RAG, Data Engineering, Anthropic Partner, LLM Security |
|---|---|
| Core Value | A pod of two to four Forward Deployment Engineers embedded in your team, first production release in weeks not quarters, measured readiness, free Agent Readiness Assessment, your code stays yours |
| Who it is for | Established companies $5M to $100M with no AI engineering team, and startups Seed to Series B building an AI core |
Forward Deployment Engineering
Put AI into production without building an AI team first
Your first AI workflow or product feature goes live in weeks, not quarters.
A pod of two to four Forward Deployment Engineers works inside your stack, your repos and your standups. You own the code, the evals and the runbooks from day one.
of enterprise AI initiatives never reach production.
Source: Gartner, 2025
The problem
Most AI never ships. The bottleneck is not the model, it is the engineering.
Deployment is the gap between a model that works in a notebook and a system your people use on a Tuesday. That gap is engineering, and it runs through the data, integrations and controls you already have.
Engineering depth
Research demos, not products.
Data foundation
AI is only as good as the pipes.
Embedded execution
External teams stay external.
Who this is for
Two ways in. Pick the one that sounds like you.
For established companies
$5M to $100M in revenue, growing data complexity, no AI engineering team yet.
Deploy AI into core operations without waiting to build an AI team.
See the established company trackFor seed to Series B startups
A real product, real users, and a funding milestone in front of you.
Ship the AI feature before your next round.
See the startup trackFor established companies
You have operational complexity that took years to build and cannot be replaced. What you do not have is an AI engineering organization, and hiring one is a year you do not have.
What we ship:
- A production AI capability on one high-value workflow: document automation, retrieval over your own knowledge, voice and call analytics, or an agent inside an existing process
- The data foundation underneath it: pipelines, cleanup, the plumbing AI actually runs on
- AI-native software delivery your team can keep using after we step back
Pod of 2 to 4 Forward Deployment Engineers · typical full engagement 3 to 6 months · first release in weeks, not quarters
For seed to Series B startups
You have users and a roadmap. The AI feature on that roadmap is the one your next round will be judged on, and it is the one your team has least time to build.
What we ship:
- The production AI core of your product, not a demo
- Agents and retrieval with an eval harness, so quality is measured instead of argued about
- The feature that carries your next funding milestone
Pod of 2 to 3 Forward Deployment Engineers · typical full engagement 6 to 12 weeks · first release in weeks, not quarters
Proof
Production AI we have shipped
Humonic, medical de-identification
Anonymizes thousands of records a day for HIPAA-regulated US healthcare workflows.
30,000+ records a day at 99.2% recall, replacing about 3 FTE of manual review.
US bank, call quality assessment
LLM evaluation of negotiation quality across branches, replacing manual monitoring.
100% of calls scored, versus about 2% sampled before. Manual QA effort down 85%.
Auto-dealer call analytics
Conversation analytics across more than a million calls.
90%+ accuracy at scale, flagging missed-opportunity calls and lifting booked appointments about 15%.
Soula.care, AI companion for women
Empathetic conversational AI for emotional wellbeing.
4.8 out of 5 across 1,200+ reviews. Full App Store launch in 4 months.
Three of these four are the same shape: a high-volume operational workflow with a measurable human process behind it. That is where we are strongest.
3Alica delivered an AI-powered inventory system that cut our forecasting errors by 40%.
VP Operations, Global FMCG Brand
They were the only team that could integrate with our legacy systems without a multi-year migration.
CTO, European Financial Services
The AI quality inspection system paid for itself in 4 months. Defect rates dropped significantly.
Head of Manufacturing, Industrial Manufacturer(payback period)
The unit you hire
Meet your deployment pod
Two to four Forward Deployment Engineers. One accountable lead. An agent fleet across the delivery lifecycle.
Every engagement has one named senior engineer accountable from discovery through production, your Lead FDE, working in a stable pod of two to four Forward Deployment Engineers, with data, architecture, quality and platform specialists available from our 40+ person team.
Deployment Lead owns the business outcome
Picks the workflow with your operators, defines the metric, drives adoption and the operating change that makes the system stick.
Lead FDE owns the production system
Sits in your repos, your stack and your standups. Integration, releases, evals and the production page.
Direct access to your Lead FDE. No account-management relay.
AI-native delivery is how a pod this size covers this much ground
Specs, pipelines, test harnesses, infrastructure as code, documentation and eval suites are produced by agents under senior review. This is not a slide. We run an agent fleet across our own delivery, and we build and operate agentic systems as products of our own. The same engineering practice runs inside your engagement.
Every production decision stays human owned and peer reviewed.
Coverage across the delivery lifecycle, and which decisions stay human owned:
| Discipline | Produced with agents | Owned by a human |
|---|---|---|
| Product / Analyst | Research, specs, epics and stories | Scope, priorities |
| Architect | System design, tech specs, patterns | Trade-offs, standards |
| AI Engineer | Implementation plan, code, RAG and agents, evals | Logic, quality |
| Data Engineer | Pipelines, data cleanup, foundation | Sources, correctness |
| QA | Test plan, cases, eval harness, bug fixes | Risk, release readiness |
| DevOps | Infrastructure as code, pipelines, monitoring | Rollout, approval |
How the work runs
Embedded, transparent, measurable
Daily
15-minute standup with your engineering lead, plus PRs in your review queue.
Weekly
Written status (shipped, blockers, scope) in a shared Slack channel.
Monthly
A production milestone with a measurable outcome and a burn-down.
Quarterly
Business review with your sponsor and a clear scope decision: continue, expand, or wind down. On engagements shorter than a quarter, that review happens at close.
A modern production stack: agent orchestration, hybrid RAG, and any model: Claude, GPT, or open-weight on your own infrastructure. US East and West time-zone overlap, from a team across the US and Europe.
See full stack
Agent orchestration: LangGraph, Claude Agent SDK, Temporal · Retrieval: pgvector, Pinecone, Qdrant · Models: latest Claude, GPT, Gemini, and open-weight via vLLM · Data: Snowflake, Databricks, dbt, Airflow, Kafka · Evals & observability: Langfuse, Braintrust, LangSmith.
Deployment
Built for your environment and controls
Same engineering, deployed where your compliance requires.
Your cloud, your VPC
We deploy against your accounts and your network boundary. Nothing crosses a perimeter you have not approved.
Zero-retention managed models
Claude, GPT or Gemini through enterprise endpoints with zero-retention agreements. Your code and data are never used to train a model.
Open weights on your own hardware
Open-weight models served on your infrastructure via vLLM, when the data cannot leave the building at all.
On-premise and air-gapped
We have delivered inside fully isolated environments with no external dependencies.
Model choice is made on workload, risk, cost and deployment requirements, not on a vendor relationship. We are an Anthropic Partner and model-agnostic by design.
Ownership
Your system from day one. Your operating model when you are ready.
Select and ship
One workflow, one metric, into production.
Harden and embed
Monitoring, evals, controls, runbooks and adoption inside your team. Every PR lands in your review queue from week one.
Choose how to operate
Continue with us on the next workflow, run it jointly, or take operations in house. At each business review, and at the latest when the engagement closes, your sponsor makes that call with the numbers in front of them.
All code, prompts, evals, runbooks and artifacts we build for you are your property from the first commit. Pre-existing components, open-source libraries and third-party models stay under their own licenses.
How an engagement starts
Three steps, and you can stop after any of them
Agent Readiness Assessment
A read-only assessment of one repository and its software delivery lifecycle. You get an Agent Readiness Score, the evidence behind it, your main blockers, and one recommended next step.
Validation sprint
A scoped paid sprint that proves the recommended use case is worth building, with the delivery plan and acceptance criteria that come out of it.
Forward Deployment Engineering engagement
The pod goes in, and the first workflow or product feature goes live in weeks, not quarters.
Agent Readiness Assessment, in detail
A free, read-only assessment of one repository and its software delivery lifecycle.
- One agreed repository and one software delivery lifecycle, reviewed read-only
- Your Agent Readiness Score: 11 pillars, 3 layers, 5 maturity levels
- The evidence behind the score, not just the number
- Your main blockers, ranked
- One recommended next step
- A readout session with the engineer who did the work
Most mid-market teams land between L2 and L3. We show you where you are, what L3 looks like, and the shortest path there.
What it is not: a code change, a prototype, a solution architecture, a full data and governance review, a security or compliance certification, a model and vendor selection, or a detailed business case. Those belong to the validation sprint.
Questions
Your code, your control
The questions every engineering leader asks before giving an outside team access.
What is a Forward Deployment Engineer?
A senior engineer we embed directly in your team to learn the problem and own the outcome. They ship working AI into production alongside your people, with no project managers or handoffs in between. The role comes from Palantir and is now how the leading model vendors deliver enterprise AI as well.
How fast can you get AI into production?
Your first workflow or product feature goes live in weeks, not quarters, measured from delivery kickoff once access, scope and acceptance criteria are in place. A full engagement typically runs 6 to 12 weeks for startups and 3 to 6 months for established companies, with the first release early and the rest built on top of it.
How do you protect our code from LLM exposure?
We run analysis against enterprise LLM endpoints with zero-retention agreements, or entirely inside your environment. Your code is never used to train models and never leaves an approved boundary.
Which LLM platforms do you support?
Claude, GPT, Gemini, and open-weight models. We are an Anthropic Partner and model-agnostic by design.
Who owns the code and IP?
You do. All code, prompts, evals, runbooks and artifacts we build for you are your property from the first commit. Pre-existing components, open-source libraries and third-party models stay under their own licenses, with your usage rights set out in the agreement.
How are NDAs and access handled?
We sign your NDA before the assessment begins and work within your access controls and least-privilege provisioning.
Do you work on site?
We work inside your stack: your repos, your standups, your review queue, with US East and West time-zone overlap from a team across the US and Europe.
What happens when the engagement ends?
Your team already reviewed and merged the work, so nothing is thrown over a wall. You get runbooks, the eval harness, an on-call handover and the architecture decisions in writing. You choose whether we continue on the next workflow, run it jointly, or step back.
Get in touch
Start with one operational bottleneck
Share the workflow that costs the most time, margin, or visibility today. We will review the current systems involved and suggest a practical first sprint.
Email us directly
sales@3alica.com