Proof format
Baseline -> method -> measured delta
Every case study is structured around the actual technical decision path.
AI Engineering Services
From AI research to production systems.
We help technical teams reproduce frontier papers, evaluate models, and ship research-backed AI infrastructure in weeks.
Book a Research Sprint
30-minute scoping call.
Send a Technical Brief
Async route, no meeting needed.
No sales deck. Reply within 1 business day.
Bring us a paper, model, or failing AI workflow. We return a reproduction repo, benchmarks, failure analysis, and a ship/no-ship decision.
[ 2 slots open ][ <30d handoff ][ 100% delivery ]
WORKS WITH
OpenAI
Anthropic
Qdrant
Modal
Braintrust
RESEARCH_SPRINT_BRIEF
Client objective
Ship a support-triage AI agent that resolves tickets end-to-end
Method
LangGraph planner + retrieval guardrails + tool-call critic loop
Baseline
48% task success on 1,200 historical tickets
Target
78% task success, <2% unsafe actions, p95 < 9s
Status
Week 3: shadow mode live, failure taxonomy + rollback gates in place
ARTIFACT_FLOW
paper.pdf->
reproduction_repo/->
eval_results.json->
handoff.md
Technical Trust Layer
Request anonymized artifact sampleExecution visibility
Weekly commit and benchmark changelog
You can inspect how decisions changed metrics across the sprint.
Handoff quality
Repo + eval harness + decision memo
Artifacts are packaged for your internal engineers, not presentation decks.
Stack credibility
OpenAIAnthropicLangGraphQdrantModalWeights & Biases
Case Studies
Operational outcomes, not demo metrics.
Anonymous engagements documented with timelines, baselines, and delivered artifacts.
Client
AI infrastructure startup
Team: ML platform + applied research
Timeline
21 days
Baseline
Manual QA on every agent release
Manual QA reduction
0%
Time to outcome
0d
Problem
Agent workflow failed on long-horizon tasks and had no regression evals.
Delivered
Tool-use eval harness + replay suite + model comparison pipeline
Result
60% reduction in manual QA review time and a repeatable release gate.
Stack
OpenAI / Anthropic / LangGraph / Postgres / Modal / Braintrust
Client
Vertical SaaS team shipping RAG copilots
Team: Product + retrieval engineering
Timeline
28 days
Baseline
Retrieval quality drift with no edge-case signal
Edge-case pass rate lift
0-pt
Faster incident triage
0%
Problem
Production RAG quality drifted weekly and failure cases were hard to triage.
Delivered
Dataset curation + retrieval diagnostics + answer-grading eval suite
Result
24-point lift on edge-case answer pass rate and 50% faster incident triage.
Stack
OpenAI / Qdrant / FastAPI / Postgres / Weights & Biases
What We Do
Structured technical engagements.
Three service modules designed for technical buyers who need concrete outputs and clear decisions.
ENGAGEMENT_01
Research Reproduction
We reproduce papers, test assumptions, and tell you what actually works.
Use when
Your team found a promising paper but does not know whether it works on your data.
Deliverables
- reproduction repo
- benchmark notes
- failure modes
- recommendation memo
ENGAGEMENT_02
Evaluation Infrastructure
We build evals for agents, RAG systems, model behavior, and production workflows.
Use when
Your AI system changes weekly and you cannot tell whether it got better or worse.
Deliverables
- eval harness
- test datasets
- regression tracking
- reliability report
ENGAGEMENT_03
Production Prototype
We convert validated research into a deployable technical prototype.
Use when
The method works, but your team needs a deployable path.
Deliverables
- prototype
- architecture docs
- deployment path
- handoff session
Research Sprint
30-day Research Sprint
A calm operating cadence with explicit outputs at each stage.
SPRINT_OPERATING_PLAN
Phase
Day 1-3
Output
Research brief, constraints, success metrics
Phase
Day 4-10
Output
Paper/model reproduction and feasibility test
Phase
Day 11-20
Output
Benchmarking, evals, failure analysis
Phase
Day 21-30
Output
Prototype, documentation, handoff roadmap
| Phase | Output |
|---|---|
| Day 1-3 | Research brief, constraints, success metrics |
| Day 4-10 | Paper/model reproduction and feasibility test |
| Day 11-20 | Benchmarking, evals, failure analysis |
| Day 21-30 | Prototype, documentation, handoff roadmap |
TECHNICAL PROOF
What a sprint produces
Structured artifacts your team can inspect, challenge, and ship from.
01/experiments/reproduction_repo
Reproduction repo
Paper implementation, baseline comparison, and ablation notes.
Contains
- - baseline branch
- - method branch
- - ablation notebooks
02/evals/eval_results.json
Benchmark report
Baseline vs method performance on your real use case.
Contains
- - task-level pass rates
- - cost and latency metrics
- - regression flags
03/analysis/failure_modes.md
Failure analysis
Where the method breaks, why it breaks, and what to try next.
Contains
- - edge-case taxonomy
- - root-cause hypotheses
- - mitigation options
04/handoff/production_recommendation.pdf
Ship / modify / reject decision
A clear technical recommendation with risks, cost, and next implementation step.
Contains
- - decision summary
- - risk matrix
- - next implementation step
Diagnostic Questions
Example problems we solve
Scoping prompts grouped by technical uncertainty, not generic feature categories.
MODEL SELECTION
- Which open-source model should we fine-tune?
- Can we reduce inference cost without quality loss?
AGENT RELIABILITY
- Is this agent architecture reliable enough for production?
- How do we evaluate multi-step tool use?
RESEARCH VALIDATION
- Can this paper improve our model performance?
- Which benchmark actually predicts user value?
Sprint Scope Estimator
Get a starting scope in 30 seconds.
Select your bottleneck and we suggest the right engagement model plus price range.
Differentiation
Not wrappers. Not dashboards. Research-grade engineering.
Typical AI agency
Starts with a chatbot use case
EAVAE Labs
Starts with a technical uncertainty
Typical AI agency
Ships a demo
EAVAE Labs
Ships repo, evals, and decision artifacts
Typical AI agency
Relies on prompt iteration
EAVAE Labs
Tests against benchmarked failure modes
Typical AI agency
Optimizes for launch
EAVAE Labs
Optimizes for reliability and transferability
Typical AI agency
Hands off documentation
EAVAE Labs
Hands off working systems your team can inspect
| Typical AI agency | EAVAE Labs |
|---|---|
| Starts with a chatbot use case | Starts with a technical uncertainty |
| Ships a demo | Ships repo, evals, and decision artifacts |
| Relies on prompt iteration | Tests against benchmarked failure modes |
| Optimizes for launch | Optimizes for reliability and transferability |
| Hands off documentation | Hands off working systems your team can inspect |
Engagement Models
Pricing with clear entry points.
Structured options for technical due diligence, sprint validation, and production handoff.
Research Audit
From EUR3.5k
For teams deciding whether a technical direction is worth pursuing.
- - architecture review
- - paper/model shortlist
- - feasibility memo
- - implementation roadmap
Research Sprint
From EUR12k
For teams that need a paper, model, or method tested against real constraints.
- - reproduction repo
- - benchmark results
- - failure analysis
- - ship/no-ship recommendation
Production Sprint
From EUR30k
For teams ready to turn validated research into production infrastructure.
- - production prototype
- - eval harness
- - integration plan
- - technical handoff
Scope a Technical Engagement
Transparent starting ranges. Final scope is fixed before kickoff.
Qualification
Built for teams with technical uncertainty.
Good fit
- - You have a paper, model, or architecture to validate.
- - You need evals before scaling an AI workflow.
- - You want production artifacts, not strategy slides.
Not a fit
- - You need a generic chatbot.
- - You want a no-code automation setup.
- - You are not ready to share technical constraints.
FAQ
Objections answered upfront.
Credibility
Built for technical buyers.
EAVAE Labs works directly with founders, ML engineers, and product teams who need research clarity before committing engineering resources.
I work with technical teams to reproduce research, build eval infrastructure, and turn uncertain AI methods into working prototypes. Every engagement is scoped around concrete artifacts: repos, benchmarks, failure analysis, and handoff docs.
Reproduction reposBenchmark harnessesFailure analysisHandoff docs
Know what is worth building.
Bring us a paper, model, architecture, or AI system. We'll help you evaluate it and turn the right path into working infrastructure.
Book a Research Sprint
Best for teams ready to start this month.
Send a technical brief
Best for async technical scoping.
You will get scope clarity, fit confirmation, and next steps within 1 business day.