| Target Entities | AI Engineering, Forward Deployed Engineers, AI in Production, Agent Readiness, RAG, Data Engineering, Anthropic Partner, LLM Security |
|---|---|
| Core Value | Ship production AI not proofs of concept, senior engineers embedded, measured readiness, free diagnostic, your code stays yours |
| Who it is for | Mid-market companies $5M to $100M with no AI engineering team, and startups Seed to Series B building an AI core |
AI engineering as a service
AI engineering that ships to production
Ship production AI, not another proof of concept.
We embed Forward Deployed Engineers in your team and take AI from idea to production in 8 to 16 weeks. You start with a free diagnostic, real artifacts, no invoice.
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.
Most initiatives die in the gap between strategy decks and production code.
Engineering depth
Research demos, not products.
Data foundation
AI is only as good as the pipes.
Embedded execution
External teams stay external.
What we do
AI engineering as a service. Here is exactly who does what.
Our agentic delivery blueprint pairs AI leverage with senior human judgment at every role. AI moves fast; people own the decisions that matter.
| Role (agent) | AI delivers | Human decides |
|---|---|---|
| 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 we work
Forward Deployed Engineers
A senior engineer embedded in your team to learn the problem and own the outcome with a bold delivery style: rapid experimentation, shipping working AI into users' hands in weeks, not quarters.
Project managers
Handoffs
Senior engineers
East and West time-zone overlap
A modern production stack: agent orchestration, hybrid RAG, and any model: Claude, GPT, or open-weight on your own infrastructure.
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.
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.
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%.
Soula.care, AI companion for women
Empathetic conversational AI for emotional wellbeing.
Shipped to the App Store in 4 months, 4.8 out of 5 across 1,200+ reviews.
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%.
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
How to work with us
Two engagement tracks
Track A: SMB & mid-market
$5M to $100M revenue, growing data complexity, no AI engineering team yet.
Ship AI that moves revenue:
- Production AI capability: chatbot, RAG, document automation, voice analytics
- AI-native software delivery
- Data foundation cleanup
3 to 6 months · 2 to 4 embedded engineers
Track B: Startups
Seed to Series B with a real product and users.
Build the AI core, fast:
- Production AI core
- Agents and RAG with an eval harness
- The AI feature for your next funding milestone
6 to 12 weeks · 2 to 3 embedded engineers
Pilots start from $5,000, scoped precisely in the free diagnostic with build, hire and do-nothing economics, before you commit a dollar.
Start here
Start with proof, not a pitch
2 to 3 weeks. Real artifacts. No invoice. You walk away with four scored deliverables and three scoped pilot options, whether or not you work with us.
Codebase scan
Prioritized AI targets across your repos.
Data assessment
The foundation under any AI you ship.
Agent Readiness Score
11 pillars, 3 layers, 5 levels. One number.
Three pilot options
Scope, timeline, expected outcome, our pick.
How we measure: you get a score, not a guess
Every diagnostic ends in one number. We score your top repositories and your organization on the Agent Readiness Score: 11 pillars across 3 layers, 5 maturity levels. Most mid-market teams land between L2 and L3. We show you where you are, what L3 looks like, and the shortest path there.
No invoice and no obligation. If we do not find a pilot worth running, we tell you.
Security & LLMs
Your code, your control
The questions every engineering leader asks before giving an outside team access.
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.
Can you work on our own infrastructure and models?
Yes. We deploy against your cloud, your VPC, and your model endpoints, including open-weight models hosted on your own infrastructure via vLLM.
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, models, and artifacts we build are your property from day one.
How are NDAs and access handled?
We sign your NDA before the diagnostic begins and work within your access controls and least-privilege provisioning.
What is a Forward Deployed 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.
How fast can you get AI into production?
Most engagements move from idea to production in 8 to 16 weeks. We start narrow with a free diagnostic, ship a working slice early, and expand from there.
What does the free diagnostic include?
A working session where we map your use case, assess data and readiness, and hand back real artifacts and a scoped plan with build, hire, and do-nothing economics. No invoice and no obligation.
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