| Target Entities | AI Engineering, Forward Deployed Engineers, AI in Production, Agent Readiness, AI Maturity, RAG, Data Engineering, Anthropic Partner |
|---|---|
| Core Value | Ship production AI not proofs of concept, senior engineers embedded, measured readiness, free diagnostic, AI and data as one team |
| 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 for AI-native organizations
Ship production AI, not another proof of concept
We embed senior engineers in your team and take AI from idea to production in 8 to 16 weeks. You start with a free diagnostic, not a pitch.
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. There are three usual reasons.
Engineering depth
Research demos, not products
Research teams ship prototypes. Internal IT lacks AI specialists. Vendors hand over slides instead of code. The result is months of proof-of-concept theater with no business outcome.
Data foundation
AI is only as good as the pipes
Most companies do not have a clean data layer ready for AI. Without curated data, AI features are hard to ground, evaluate and trust. The plumbing matters more than the model.
Embedded execution
External teams stay external
Vendors work in isolation, far from your repo. The AI never becomes part of the product, and when the contract ends, the capability has not transferred to your team.
What we do
AI engineering as a service. We earn the right to scale.
Three phases, each with a gate before the next begins. Senior team, production code, no workshops.
1. Diagnose
We scan your codebase, Jira, git history and CI/CD, surface the highest-leverage targets, and score your repos and your organization on readiness. You leave with real artifacts and three scoped pilot options.
2. Pilot
An embedded team builds the first AI capability against a real production target with a measurable outcome. The gate is simple: did it ship, and did it move the needle.
3. Scale
Your team takes over delivery on proven rails. We stay as a senior backstop, with quarterly reviews and a clear scope decision each quarter: continue, expand or wind down.
How we measure
You get a number, not a vibe
We use industry frameworks as scorecards and target states, never as workshop products. We baseline where you are, then raise the score through shipped PRs.
AI Readiness Model
Five levels across four areas. Most mid-market sits between level 2 and level 3. We measure where you are and what it takes to move up.
Data foundation
Quality, governance, lineage and refresh velocity.
Analytics and decisions
From manual reports to real-time decisioning.
AI in operations
From browser tabs to AI agents in production.
Operating model
Roles, processes, ownership and AI literacy.
Agent Readiness Score
Eleven pillars across three layers, mapped to five maturity levels. Baselined in Diagnose, then raised through shipped PRs in Pilot and Scale.
Pillars
Levels
Target
How we work
Forward Deployed Engineers
A senior engineer embedded in your team to learn the problem and own the outcome. The model Palantir pioneered, now reflected in forward-deployed roles at OpenAI and Anthropic. We own the work from first prototype to production, measured by adoption and impact, not hours billed.
Project managers
Handoffs
Senior
East and West time-zone overlap
The production AI stack we deploy
End-to-end ownership, with no handoff loop between AI, data and infrastructure vendors.
Surface
Agents, copilots, chat, voice and search embedded in your product or internal tools.
agents · copilots · chat · voice · search
Agent orchestration
Durable agentic workflows that route requests, call tools and chain models.
LangGraph · Claude Agent SDK · OpenAI Agents SDK · Temporal
Models
Best-fit model per task, cost and latency optimized, eval-driven.
Claude Opus 4.x · GPT-5.x · Gemini 2.x · open-weight via vLLM
Retrieval and tools
The knowledge layer connecting models to your data and systems.
hybrid RAG · pgvector · Pinecone · Qdrant · rerankers · MCP
Data and evals
Warehouse, pipelines and streaming, plus an eval harness and tracing in production.
Snowflake · Databricks · dbt · Airflow · Kafka · Iceberg · Langfuse
Why us
Four ways we differ
Most AI shops are methodology consultants, staffing firms or research spinoffs. Few combine senior engineering with measurement.
Partner
Anthropic Partner
Member of the Anthropic Partner Network, with Claude implementation practices and reference architectures.
Experience
15+ years in production
Not a research lab, not a bootcamp. Real systems shipped in retail, healthcare, banking, telecom and fintech.
Stack
AI and data, one team
Most AI stalls at the data layer. We build the warehouse, pipelines, agents, RAG and evals as one system.
Measurement
A number, not a vibe
Industry frameworks used as scorecards, so progress is measured and shipped, not workshopped.
How to work with us
Two engagement tracks
Same Proof-of-Value methodology, same Forward Deployed model. Different scope, different pace.
Track A / SMB and mid-market
Ship AI that moves revenue
$5M to $100M revenue, growing data complexity, no AI engineering team yet. We extend your team, we do not replace it.
- Production AI capability: chatbot, RAG search, document automation, voice analytics
- AI-native software delivery and legacy modernization with AI
- Data foundation cleanup so AI features stay reliable
- 3 to 6 months per scope, 2 to 4 embedded engineers, plus your team trained to maintain it
Track B / Startups
Build the AI core, fast
Seed to Series B with a real product and users. Own the stack you compete on, do not bolt a tool onto a legacy flow.
- The production AI core you compete on, not a demo agent on top of an old flow
- Agents and RAG with an eval harness, plus the data foundation underneath
- Ship the AI feature that de-risks your next funding milestone
- 6 to 12 weeks per milestone, 2 to 3 embedded engineers, quarter by quarter
Start here
Start with proof, not a pitch
Two to three weeks. Real artifacts. No invoice. If we cannot find a worthwhile pilot, we tell you.
Four scored artifacts
- Codebase scan with prioritized AI targets
- Data foundation assessment
- AI Maturity Scorecard across ten dimensions
- Agent Readiness Score for your top repos, eleven pillars
Three scoped pilot options
- Three pilot proposals with concrete scope, timeline and expected outcome
- Build, hire and do-nothing economics for each
- Our recommendation: which one to start with, and why
Cost
Duration
Scored outputs
Pilot options
No invoice and no obligation. If we do not find a pilot worth running, we tell you.
Proof
Production AI we have shipped
A few examples across regulated and consumer domains. A full portfolio of 15+ cases is available on request.
Medical de-identification
Anonymizes thousands of records a day for HIPAA-regulated US healthcare workflows, replacing manual redaction at scale.
Call quality assessment
LLM evaluation of negotiation quality across branches, replacing manual monitoring that was never feasible by hand.
AI companion for women
Empathetic conversational AI for emotional wellbeing. Shipped to the App Store in four months with a 4.8 out of 5 rating.
Sales automation
Conversational AI handles repetitive buyer questions and books meetings, reducing drop-off from slow answers.
Plus auto-dealer call analytics at 90%+ accuracy over 1M+ calls, environmental compliance, a manufacturing knowledge base, and more.
The team
Senior engineering team
Multi-disciplinary specialists across AI, data and platform engineering. Every engagement is led by a senior engineer.
AI engineering
- Valiantsin (Lead): LangChain, RAG, MCP
- Sergei: Voice, computer vision, edge AI, 9 years
- Alexander: BigQuery, Whisper, 8 years
Data engineering
- Galina (Architect): data warehouse, Snowflake, 25 years
- Elena (Senior): Snowflake, Airflow, 10 years
- Ekaterina (Analytics): Tableau, Power BI, 6 years
- Ekaterina (Data): dbt, Data Vault, 8 years
Platform engineering
- Pavel: FastAPI, Kubernetes, gRPC, 10 years
- Maria: Python, Go, microservices, 5 years
- Alex: Databricks, Spark, Kafka, 5 years
How it feels to work with us
Embedded, transparent, measurable
Daily
Standup with your engineering lead, 15 minutes, plus frequent PRs in your review queue.
Weekly
Written status: shipped, blockers, scope. A shared Slack channel with full team access.
Monthly
A production milestone with a measurable outcome, plus a burn-down and economics report.
Quarterly
Business review with your executive sponsor and a clear scope decision: continue, expand or wind down.
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