AI · LLM · ML · DATA CONSULTING
95% of generative-AI pilots never show measurable business impact (MIT, 2025). We are built backwards from that statistic: every system we ship arrives with its own evaluation harness, governance gate and audit trail — so value is measured, not promised.
What We Sell
Every service below ships with the same proof pattern we used to earn it: a measured baseline, a controlled change, and a number you can interrogate.
Flagship — rare in the market
We replay your AI system’s own historical decisions counterfactually and answer the question most teams can’t: does each layer add or destroy value — in dollars per decision?
Grounded answers over your documents, tickets and databases — hybrid retrieval, reranking, citations, and explicit refusal when the knowledge doesn’t exist. Tuned vector stores, not toy demos.
LoRA/QLoRA fine-tunes that lock behaviour and cut inference cost — exported to quantized GGUF and served wherever your data must stay private.
If you can’t measure it, you can’t ship it. Reproducible eval harnesses that score schema compliance, task accuracy, calibration (ECE) and retrieval value — wired into CI so regressions get caught before users do.
Your next hire isn’t human
Assistants and agentic workflows that execute across your tools — CRM, ticketing, knowledge bases — through typed MCP tool layers and structured-output contracts, orchestrated on schedules with human escalation where it belongs.
Governance encoded in software, not PDFs: guardrail models that size, block or approve every AI recommendation against your rules — with a deterministic engine as fail-safe fallback and automated auditors watching calibration drift.
Your pilot worked in the demo and dies in production. End-to-end forensic validation finds the silent failures happy-path testing misses. Precedent: four silent failures (fake data node, collapsed batches, dead cron) found and fixed same-day; zero-downtime credential rotation with consumer-by-consumer verification.
Most AI failures are data failures wearing a costume. Seven years of enterprise data-governance practice — full-lifecycle Stibo STEP and Contentserv implementations — make your master data trustworthy enough for AI to inherit. Precedent: 25% efficiency gains, $500K in client savings.
🔒 Private by default
Field Reports
These are sanitized field reports from live deployments we own end-to-end. Ask us anything about them on a call — every number survives interrogation.
A production market-analysis assistant grounded in five years of market data. Retrieval over a tuned pgvector/HNSW store feeds a fine-tuned 4B model served locally; a 750-case eval harness and a True-RAG ablation prove both components earn their place.
“Retrieval isn’t decoration. Remove it and direction drops 2.7 points while calibration degrades — the ablation proved the architecture, not the demo.”
| Schema-valid output (750 held-out cases) | 100% |
|---|---|
| Directional accuracy (fine-tuned) | 97.6% |
| RAG ablation lift (base model) | +2.7pts direction · ECE 0.335→0.306 |
| kNN query latency (tuned HNSW) | 0.93 ms |
| Regression suite | 29 passing tests incl. index verification |
A live approval layer was quietly destroying value. From frozen inference caches alone — zero new compute — counterfactual replay proved approved decisions earned −0.175%/trade while rejected ones would have earned +0.151%. Ablation controls isolated the cause; a reversible, provenance-marked guardrail shipped to production inside 24 hours.
| Approved vs rejected expectancy | −0.175% vs +0.151% per trade |
|---|---|
| Deterministic gate-flip control | +11.1pp profit factor (0.86→1.14) |
| Simulation scale | 906 events · 898 cached inferences · 0 parse failures |
| Finding→production patch | <24 hours, zero downtime |
A three-model closed loop on live market data: analyst → risk gate → outcome scoring → auditor, on hourly and 6-hour schedules. Every AI recommendation is gated against an encoded rules engine with a deterministic fallback; the auditor watches calibration drift and bias and files its own reports.
| Risk gate decision match vs rules engine | 89.3% (607 held-out) |
|---|---|
| Auditor schema pass | 98.9% |
| Unattended accumulation (verified) | 89 analyses → 88 gates → 80 scored → 5 audits |
| Leak-free dataset quality gates | 7/7 PASS, byte-identical regeneration |
Full-lifecycle master-data implementations (Stibo STEP, Contentserv) for national retail enterprises: data models, workflows, integrations and JavaScript business-rule engines — the foundation any serious AI program stands on.
| Efficiency gain | ~25% average |
|---|---|
| Client savings identified | $500K |
| Scope delivered | Data models · workflows · UI config · business rules · EA standards |
Strategy internals stay confidential — the same courtesy applies to yours.
Skeptical? Good. Bring FR-02 to a call and try to break it →
Proof Lab
Before we sell a service, we build it and measure it on a real mid-market e-commerce dataset (40K customers / 250K orders). Source code, reports and methodology are open for walkthrough on a call — every number below was generated by code you can read.
Proves: Private by default
A quantized open-weight LLM served locally on client-style hardware, benchmarked against an external API on identical tasks.
| Task accuracy (local vs external) | 84% at equal quality bar |
|---|---|
| Latency | 13 ms vs 404 ms |
| Data egress | Zero bytes leave your environment |
Proves: Governance as software
A 12-category audit engine run against 292,000 real records. It returned FAIL — exactly what an honest auditor should say when controls are missing.
| Records audited | 292,000 in ~4 seconds |
|---|---|
| Findings | 45 total · 12 high-severity |
| Output | Ranked remediation roadmap (0–30d / 30–90d / 90–180d) |
Proves: Regulatory work with citations
A profile-driven gap-analysis engine covering 15 obligations across three jurisdictions — every item cited to a primary source we personally verified.
| Obligations mapped | 15 across EU / US federal+states / Canada |
|---|---|
| References | 17 sources, all fetched & verified Aug 2026 |
| Demo verdict | E-commerce profile: 19.2% posture, 8 gaps |
Proves: Evals that catch liars
We built a churn pilot the way teams actually build it — and our diagnostic proved its headline metrics were phantom before anyone funded a rollout.
| Reported by pilot | AUC 0.765 · 2.4× lift |
|---|---|
| After leakage probe | AUC 0.504 — a coin flip |
| Verdict logic output | DO NOT FUND — saved a doomed deployment |
Proves: Training with audit evidence
A compliance-grade training program mapped to the EU AI Act Art. 4 literacy duty (in force since Feb 2025), ending in an auto-scored certification your compliance officer can file.
| Curriculum | 6 modules × 3 delivery tracks |
|---|---|
| Assessment | Auto-scored engine, runs offline, unique-ID certificates |
| Mapping | Primary sources verified Aug 2026 |
All five artifacts live under version control with their full reports. Ask to see any of them run on a call →
See It Run
Every Proof Lab artifact runs live on our systems. Book a 15-minute walkthrough and we’ll show you:
No sales pitch. Just an engineer showing you the code.
Corporate Training · New
EU AI Act Article 4 requires providers and deployers to ensure sufficient AI literacy of staff operating AI systems — a duty in force since February 2025. Most training ends with a smile sheet. Ours ends with scored certificates, item-level records and a source-verified compliance mapping you can hand to an auditor.
3 hoursleadership track
Most requested
1 dayteam leads & analysts
2 daysall staff operating AI
📜 What makes it compliance-grade
Planning an AI rollout or already operating one? Scope a cohort →
Why Us
Consultancies describe the future. We bring measurements. Here’s how an engagement with us differs from the usual pitch.
How We Work
Every phase produces a measurable deliverable you can inspect, interrogate, and take to a stakeholder meeting.
Counterfactual replay of your existing decisions, data-readiness review and workflow mapping.
Pragmatic architecture: RAG vs fine-tune vs both, local vs cloud serving, governance-by-design.
Implementation with exit gates: schema-valid outputs, held-out eval thresholds and E2E validation against real data.
Scheduled outcome scoring, automated calibration/bias audits, scheduler verification and quarterly value reviews.
Who We Serve
Four workflows we know first-hand — because we built, broke and fixed these systems ourselves before bringing them to your industry.
AI-assisted decisions nobody can vouch for — and regulators asking why.
Market-data RAG, risk-gated recommendations, calibration monitoring and decision-value audits. We speak profit factor, drawdown and expectancy — fluently.
AI initiatives inherit messy product, customer and supplier data.
Master data management, golden records and governance frameworks from seven years of enterprise retail-platform practice — so agents inherit trust instead of garbage.
Support volume grows with headcount, and generic chatbots hallucinate policies.
Policy-aware support agents grounded in your KB with governance gates blocking out-of-policy promises, calibrated confidence driving automatic human escalation, and conversation-quality audits after every window.
The demo worked, the rollout didn’t, and leadership is losing patience.
Pilot rescue via forensic E2E validation, label autopsies, ablation-driven error attribution and a rebuild path with acceptance gates leadership can watch move.
Recognize your workflow here? Talk it through with an engineer, not a salesperson →
Market Context
The numbers behind the shift — and why proof-first delivery is where the market is heading.
Engagements & Pricing
Indicative ranges so you can budget before we ever talk. Final scope — with acceptance numbers written down — is agreed before you commit.
$8K–$25Kfixed fee
Most common first build
$25K–$60Kscoped sprint
from $5K/momonthly
Risk reversal: if an audit finds no measurable improvement opportunity, you keep the Value Map and owe nothing further. Market context: senior AI consulting runs $200–400/hr; our fixed fees start below the typical POC band.
FAQ
The questions every serious buyer asks before engaging an AI consultancy — answered the way we'd answer them on a call.
Default to RAG for knowledge that changes — it’s the right first choice for roughly 80% of enterprise LLM applications. Fine-tune to lock behaviour, tone and output schema that prompting can’t hold, or to distil a frontier model into a cheaper one. In production we usually combine both: retrieval for facts, fine-tuning for behaviour.
Three instruments: an eval harness measuring schema compliance, accuracy and calibration on held-out cases; P&L-in-the-loop simulation replaying your model’s decisions through your actual business rules; and counterfactual replay of cached decisions quantifying whether each layer adds or destroys value.
Yes — model-agnostic by design: OpenAI, Anthropic, Gemini or open-weight models served locally for privacy. Integration runs through typed tool layers (MCP), structured-output contracts and standard APIs across PostgreSQL, FastAPI, n8n, Docker, Streamlit, and your CRM/ticketing stack.
Yes — AI troubleshooting and pilot rescue is a core service. Forensic E2E validation catches silent failures demos hide — in one campaign: a stubbed data node returning fake JSON, collapsed batch processing, single-item handling and a dead scheduler, all fixed same-day. Label autopsies and ablation controls then tell us whether damage lives in your filters or your model.
Systems ship with self-scoring loops: scheduled outcome scoring, automated audit reports for calibration drift and bias, scheduler verification and lineage hygiene. A fractional-retainer option keeps your system monitored, audited and improving month over month.
Fixed fees, published on the page: Decision-Value Audits run $8K–$25K, PoC build sprints $25K–$60K, fractional retainers from $5K/mo. For context, senior AI consulting market rates are $200–400/hr with typical fixed POCs at $15–40K. Every engagement is fixed-scope — no open-ended hourly billing.
Not unless you want it to. We deploy quantized open-weight models served locally (GGUF on your hardware), so sensitive data never leaves your environment — a pattern we already run in production. Where frontier APIs do win, we say so plainly and put governance gates around them. Either way you get decision logs, lineage and audit trails fit for compliance review.
Platforms sell capability; nobody sells you proof it works on your data. We’re model-agnostic and platform-agnostic: if a vendor product is the right answer, we’ll architect around it — but our eval harness will show you that with numbers before you sign. What platforms don’t include: acceptance criteria as measurable gates, ablation studies proving each component earns its place, and self-auditing loops after handover.
Yes — that’s the point of our Proof Lab. Five working artifacts (private LLM deployment, governance audit engine, US/EU/Canada compliance checklist, pilot rescue diagnostic, and a compliance-grade AI fluency training program) were built and measured on a real 292,000-record e-commerce dataset before we advertised anything. Source code, generated reports and methodology are open for walkthrough on a call — every number on this site traces to a measured run.
A free 30-minute scoping call. If there’s a fit, we propose a fixed-fee Decision-Value Audit or a scoped build — with the deliverables and acceptance numbers written down before you commit.
Free · no call required
The 12 checks our forensic validation runs on every stalled pilot — including the four silent failures (fake data node, collapsed batches, dead scheduler) that killed our own pipeline until we caught them same-day.
Get Started
Tell us what you’re trying to automate or audit. You’ll get a focused, practical perspective — and if we engage, the acceptance criteria are numbers, not adjectives.