AI Strategy & Discovery
Roadmaps grounded in your data, your constraints, and the technology that actually works. No buzzword bingo.
- Use-case prioritization
- Build vs. buy analysis
- ROI & risk modeling
Now booking Q3 2026 engagements
Production-grade AI & ML
engineered for the real world.
We are a senior AI & Machine Learning consultancy. From data foundations to LLM-powered products, we partner with teams to architect, build, and ship systems that perform — at scale, in production, with measurable ROI.
expertdata · pipeline.live
runningTrusted by data & engineering teams at
NORTHWIND QUANTUMlabs HELIX VERTEX/AI NEBULA·CO STRATOS
/ services
Everything you need to take AI from idea to production.
Six focused practices, one senior team. We embed alongside your engineers — not above them — and leave you with systems your team can own and extend.
Most requested
LLM & Generative AI
RAG, agents, fine-tuning, and evaluation pipelines. We build LLM systems that are reliable, observable, and cost-aware.
- Agentic workflows
- Retrieval & embeddings
- Eval harnesses + guardrails
Data Engineering
Modern stack pipelines, lakehouses, and feature stores — the unglamorous foundation that makes everything else possible.
- ELT & streaming
- Lakehouse architecture
- Quality & lineage
ML Engineering
Custom models, rigorous training pipelines, and the boring discipline of reproducibility. From research notebook to production binary.
- Recommender systems
- Forecasting & CV
- Experimentation platforms
MLOps & Platform
CI/CD for models, drift monitoring, cost dashboards. The infrastructure that lets your team ship models like software.
- Model registry & serving
- Observability & drift
- FinOps for AI workloads
Staff Augmentation
Senior engineers and ML scientists embedded into your team — three engagement schemas to fit how you run.
- Self-managed FTEs
- Managed FTEs
- Project & team management
/ approach
A predictable path from signal to system.
Our delivery model is opinionated by design. Four phases, weekly demos, and clear exit criteria — so you always know what you're getting and when.
- 01
Discover
Two-week deep dive into your data, stack, and goals. We come out with a prioritized roadmap and a sharp problem statement.
2 weeks - 02
Prototype
Working spike against real data. We optimize for learning, not polish — to de-risk the hardest unknowns first.
3–4 weeks - 03
Build
Production engineering: pipelines, evaluation, observability, and the deployment infrastructure to support it all.
6–12 weeks - 04
Hand off
Documentation, paired sessions, and a fully-owned system. Your team runs it; we stay on call.
2 weeks
/ selected work
Outcomes, not demos.
A small sample of recent engagements. Names changed where confidentiality demands it; the numbers are real.
Replaced a 30-person ops team's playbook with a single agent.
Built an agentic system that triages support tickets, drafts responses, and executes refunds — with full audit logs and a hard human-in-the-loop boundary on anything over $500.
87% auto-resolution
4.2× faster TTR
$1.8M annualized savings
Rebuilt a recommender stack and lifted revenue per session by 19%.
Two-tower retrieval + cross-encoder ranking, served at sub-50ms p99. Shipped with a feature store, drift monitoring, and the eval framework the team needed to keep iterating.
+19% RPS
42ms p99 latency
3 squads enabled
Deployed a CV pipeline that catches anomalies radiologists miss.
Co-designed with clinicians; HIPAA-compliant from day one. Shadow-mode rollout for six months, then full production with a built-in escalation workflow.
96.4% recall
0 PHI incidents
11 sites in production
Cut a Snowflake bill by 64% without slowing a single dashboard.
Audited query patterns, refactored hot models, and introduced a workload-aware materialization strategy. Paired with the data team for two months to make it stick.
−64% warehouse cost
2.1× dashboard speed
0 regressions
$420M+ in measurable client outcomes
28 senior engineers across 4 timezones
100% of projects shipped to production
12 yrs average team experience
/ use cases
Nine places where AI actually pays back.
These are the patterns we've shipped enough times to know the pitfalls. Hover any node to see the techniques we reach for.
Tool-agnostic by design. We pick what fits your stack and your constraints — never what's on a vendor sheet.
Churn Prediction Pricing Intelligence Recommendation Engines Price Optimization AI Strategy Anomaly Detection Impact Evaluation A/B Tests & Online Experiments Staff Augmentation
Hover or drag a node to explore the use cases
Churn Prediction
Predicting who's about to leave so retention spend goes where it actually pays back.
XGBoostSurvival AnalysisCohort AnalysisSHAPFeature Stores
Pricing Intelligence
Knowing what the market is doing — competitor prices, demand signals, willingness-to-pay.
Web ScrapingElasticity ModelingTime-Series ForecastingSnowflakeEmbeddings
Recommendation Engines
Personalized ranking that lifts engagement, revenue per session, and long-term retention.
Two-Tower ModelsCross-Encoder RankingVector SearchCollaborative FilteringA/B Testing
Price Optimization
Setting the price that maximizes the objective — revenue, margin, or strategic share.
Bayesian OptimizationMulti-Armed BanditsDemand CurvesConstrained SolversCausal Inference
AI Strategy
Roadmaps, prioritization, and the unglamorous work that makes the technical work worth doing.
Use-Case DiscoveryBuild vs BuyROI ModelingStakeholder AlignmentRoadmapping
Anomaly Detection
Catching fraud, system failures, and outliers in production data — fast, with low false positives.
Isolation ForestAutoencodersStreaming (Kafka)Statistical ControlDrift Detection
Impact Evaluation
Custom experiment design and causal evaluation of interventions when randomized rollouts aren't possible.
Difference-in-DifferencesSynthetic ControlInstrumental VariablesRegression DiscontinuityPropensity Score Matching
A/B Tests & Online Experiments
Online controlled experiments wired into your product — designed, powered, and analyzed correctly.
Sequential TestingCUPED Variance ReductionBayesian A/BPower AnalysisStratified Randomization
Staff Augmentation
Senior engineers and ML scientists embedded into your team — three engagement schemas to fit how you run.
Self-Managed FTEsManaged FTEsProject & Team Management
Expert Data didn't just build us a model — they rebuilt the way our team thinks about ML. Six months in, our internal engineers are shipping features I would've outsourced a year ago. That's the real return.
Sarah Mendez VP Engineering · Northwind
/ about
A small team of senior operators.
We're former tech leads, founding engineers, and research scientists from companies you've heard of. We started Expert Data because the gap between "AI demo" and "AI in production" kept eating real budgets — and we knew how to close it.
- No juniors on your project. Ever.
- Fixed-scope phases with weekly demos.
- Code, infrastructure, and docs you fully own.
- A standing 90-day post-launch warranty.
SYSTEM ONLINE UTC --:--
team28 engineers
median experience12 years
tz coverageUTC −8 → +3
active engagements9
nps · last 12mo72
response time< 4 hours
$ expertdata --status
/ get in touch
Have a problem worth solving well?
Tell us what you're working on. We'll reply within four hours with either a no, a referral, or a 30-minute call to dig in.