Available · Q3 2026 Montréal · Remote-first Founder-led

We build systems
that think
— and ship.

Daath.ai is a strategic consultancy in artificial intelligence and IT infrastructure. We design, train and operate LLMs, agents and cognitive products in production — from the data pipeline to deployment, with governance, security and architectural clarity.

Book free assessment → Explore capabilities ↓
Daath.ai
67%
The gap
of enterprises have deployed LLMs — but fewer than 30% run reliably in production.
70%
The pilots
of AI pilots never reach production. Usually not for technical reasons.
5–10×
The speed
faster time-to-value with a specialist boutique than with a Big Four engagement.
$30B+
The market
global AI consulting spend in 2026, growing 20%+ per year. Choose the partner wisely.
Production-grade RAG Agents in production Fine-tuning & distillation MCP & tool design AI observability EU AI Act ready Sports & football AI Production-grade RAG Agents in production Fine-tuning & distillation MCP & tool design AI observability EU AI Act ready Sports & football AI
01 Manifesto

Applied intelligence, not announced.

We live in an age where every company claims to be «AI-powered». Few translate that promise into systems that work at three in the morning, under load, with messy data, under regulation, under pressure. Daath.ai exists for that gap.

«Daath» is the hidden sefirah — the knowledge that connects the tree. We build the invisible infrastructure that turns models into products.

We work from architectural diagnosis to continuous operation: we audit your stack, design the right adaptation strategy (RAG, fine-tuning, distillation, agents), implement with engineering rigor, and operate with real observability. No hype, no demo that won't scale.

Every project starts with a hard question: does this problem actually need AI? When it does, we deliver systems that measure, improve and account. When it doesn't, we say so plainly — and help solve it with the right tool.

02 Why Daath.ai

The difference is who you talk to.

Four commitments
/ 01

Founder-led. Always.

The person you meet in the first call is the one who architects your system. No junior handoffs, no revolving door of associates. Senior signature on every deliverable.

/ 02

Production-grade or nothing.

We don't ship demos. Every engagement targets a system that runs under real load, with real users, with observability and rollback plans built in from day one.

/ 03

Compliance by design.

EU AI Act, LGPD, GDPR, SOC 2, HIPAA — we design the governance layer in parallel with the model layer. Regulated industries welcome. Auditability is not an afterthought.

/ 04

Handoff, not lock-in.

Every project ends with your team owning the code, the runbook and the eval harness. We measure success by the capability we leave installed — not the retainer we extend.

03 What makes us different

AI sized to the problem.

8 differentiators · 5 foundations
/ 01

Right-sizing, not over-engineering

Honest diagnosis before any architecture. About 1 in 4 Discovery engagements ends with «this doesn’t need AI» — solved with a query, a workflow rule or a better UI. You pay for four weeks of diagnosis instead of six months of the wrong build.

/ 02

ROI mapped before the build

Every use case enters with a cost estimate, an expected value and a kill criterion — before the first line of code. If the numbers don’t close, the project doesn’t start. The spreadsheet is yours to challenge.

/ 03

FinAI: the AI bill under control

FinOps applied to AI: cost per token, per request, per use case, tracked from day one. Inference, GPU, caching, routing to smaller models, distillation. The invoice drops and you know exactly which change did it.

/ 04

Harness Engineering: agents under control

Harness Engineering treats the agent as a component under control: explicit action limits, permission boundaries, continuous evals and supervision designed into the architecture. Never a black box wired straight into production.

/ 05

Loop Engineering: agents that improve in production

We design the feedback loops of the agentic system: automated evals, decision telemetry, trajectory correction. The month-6 agent is measurably better than the day-1 agent — and we can show you the curve.

/ 06

AI on event-driven architectures

An agent living outside your systems is a demo. We integrate agents into the company’s real architecture — Kafka, schema contracts, sagas, microservices — without breaking what already runs. Few consultancies work both sides of that boundary.

/ 07

Governance sized to the risk

EU AI Act, LGPD, GDPR, Quebec Law 25 — applied in proportion to the actual risk of the use case. A high-risk system gets audit trails and human-in-the-loop; an internal summarizer doesn’t get the theater. Both survive the regulator.

/ 08

Simplicity as an engineering metric

Fewer components, fewer models, fewer failure points. We count what we did NOT build as a deliverable — every avoided component is maintenance your team never pays for.

How we’re built
/ 09

Authorship, not resale

We publish the patterns we apply: a bilingual technical series and public repositories anyone can read. The method you hire is the published one — verifiable before you sign anything.

/ 10

Open weights, real on-prem

Fine-tuning (LoRA/QLoRA), distillation and serving of open models — Llama, Qwen, Mistral — on your infrastructure. For data-sovereignty constraints, or to stop paying per token for a capability you can own.

/ 11

Evals before features

Nothing ships without an evaluation pipeline: domain benchmarks, automated regression, drift monitoring. If it can’t be measured, it can’t be operated.

/ 12

Handoff as the success metric

Every project ends with your team owning the code, the runbook and the eval harness. We track how long you still need us after delivery — the target is a number that goes down.

/ 13

Montréal, multilingual, Quebec-ready

Delivery in English, French, Portuguese and Spanish. Quebec Law 25 handled as a design requirement — local companies get AI without exporting data. We’re in the same time zone as your DPO.

04 The AI Stack

From intelligence to deployment.

Click any node to explore
AI INTELLIGENCE ML LEARNING Symbolic EXPERT Robotics AUTONOMY Supervised LABELED Unsupervised CLUSTERS Reinforce. REWARD Deep Learning CNN CONVNETS Transformers ATTENTION LLMs FOUNDATION Computer Vision NLP LANGUAGE Generative RAG · AGENTS
Currently selected AI

Artificial Intelligence

The umbrella discipline encompassing any system that mimics cognitive functions — learning, reasoning, perception, planning.

Algorithms · techniques · approaches
Core domain Sub-field Architecture Specialization
05 The Data Stack

From raw data to AI-ready.

Click any node to explore
Data PLATFORM Data Eng. PIPELINES Governance TRUST Analytics BI Batch ETL·ELT Streaming CDC Orchestr. DAGS Storage Layer Warehouse SQL Data Lake OBJECT STORE Lakehouse DELTA·ICEBERG Open Formats Vector EMBEDDINGS Features STORE
Currently selected Data

Data Platform

The end-to-end foundation that turns raw data into reliable products — for analytics, operations and AI.

Algorithms · techniques · approaches
Core domain Sub-field Architecture Specialization
06 Domain-Driven Design

From domain to distributed systems.

Click any node to explore
DDD DOMAIN-DRIVEN Strategic DESIGN Language UBIQUITOUS Tactical PATTERNS Contexts BOUNDED Mapping CONTEXT ACL ISOLATION Micro services APIs REST·GRPC Event-Driven KAFKA Event Sourcing Sagas CHOREOGRAPHY CQRS READ·WRITE Projections READ MODELS
Currently selected DDD

Domain-Driven Design

Designing software around the business domain — language, boundaries and models that match how the company actually works.

Algorithms · techniques · approaches
Core domain Sub-field Architecture Specialization
07 How we work

Four phases, one accountable partner.

Method & cadence
01

Discover

Architectural diagnosis. We audit data, stack, workflows and regulatory context to define what to build — and whether AI is the right answer.

Week 1–4
02

Architect

Reference architecture with model choice, retrieval strategy, guardrails, cost envelope and observability plan. Defendable by leadership.

Week 3–6
03

Build

Bi-weekly sprints with demos. Evals from day one, CI/CD from day two, production hardening before we call anything done.

Week 6–24
04

Operate & handoff

Runbooks, on-call rotation, drift monitoring. Your team takes ownership on your timeline — we stay as thin backup, not gatekeeper.

Ongoing
08 Capabilities

From diagnosis to deploy.

08 areas · modular offerings
01 / Strategy

AI Consulting

Architectural diagnosis: data maturity, technical viability, stack selection, ROI estimation. Adoption roadmap in 30/60/90 days.

DiscoveryRoadmapROI
02 / Engineering

LLMs & Agents

Production-grade RAG, fine-tuning (LoRA/QLoRA), agents with tool use, MCP servers. Architectures that hold under load with real governance.

RAGFine-tuningAgentsMCP
03 / Product

Cognitive Products

From MVP to multi-tenant SaaS. Interfaces that converse, copilots that execute, automations that learn from real client usage.

SaaSCopilotsUX/AI
04 / Infra

Infrastructure & IT

Inference serving, GPU orchestration, data pipelines, observability. Kubernetes, Vector DBs, cost-efficiency. Cloud, on-prem or hybrid.

K8sInferenceVector DB
05 / Evaluation

Evals & Quality

Evaluation pipelines for LLMs and agents. Custom benchmarks, automated regression, bias analysis, drift monitoring in production.

EvalsBenchmarksDrift
06 / Governance

Governance & Compliance

EU AI Act, LGPD, GDPR, SOC 2. Risk classification, audit trails, model cards, human-in-the-loop by design. Ready for the regulator.

EU AI ActLGPDSOC 2
07 / Data

Data Engineering & Platforms

The data foundation for AI and for the business: batch and streaming pipelines, lakehouse, warehousing and BI. Data quality, lineage, cataloging and governance — plus vector stores, embeddings and feature stores that make your data genuinely AI-ready.

ETL/ELTLakehouseStreamingData Quality
08 / Architecture

Event-Driven & Microservices

Distributed systems that evolve without breaking: event-driven architecture, microservices and API design (REST, GraphQL, gRPC). Kafka, schema contracts and versioning, sagas and integration patterns that scale with your teams.

KafkaAPIsMicroservicesContracts
Industries we serve
Legal Healthtech Fintech & Banking Retail & E-commerce Industrial & Manufacturing Insurance Public Sector SaaS & Tech Sports & Football
Regulated industries welcome — compliance is our default, not an upsell.
Deep specialization in sports & football AI — tracking data, scouting models and match analytics.
09 Reference cases

From prototype to production.

◆ Illustrative — anonymized patterns
/ 001
Legal copilot for mass litigation
RAG over 2M+ documents · brief-drafting agent · audit trail
Legal
/ 002
LLM-powered clinical analysis platform
EHR triage and summarization · privacy-compliant · human-in-the-loop
Healthtech
/ 003
Dynamic pricing agents in e-commerce
Multi-agent orchestration · millions of SKUs · double-digit margin uplift
Retail
/ 004
Fine-tuning infra over Llama 70B
LoRA pipeline · on-prem DGX · domain-expert model handoff
Industrial
/ 005
Eval system for banking chatbot
Automated regression · thousands of scenarios · CI/CD integrated
Fintech
/ 006
Match & player intelligence for football
Tracking data & xG models · AI scouting reports · opposition analysis
Sports
10 Engagement models

Ways to collaborate.

3 modalities · flexible scope
Discovery

Dense diagnosis, clear decision.

A focused 4-week sprint to map viability, design the architecture and give your leadership team a defendable plan.
  • Technical & data audit
  • Viability & ROI analysis
  • Reference architecture
  • 30/60/90 day roadmap
  • Executive presentation
4-week sprint · Founder-led
Request proposal →
Recommended
Build

A squad shipping every sprint.

Founder + specialists delivering in bi-weekly cycles. From architecture to production deployment, with full ownership.
  • Senior team, no junior handoffs
  • Bi-weekly sprints with demos
  • Full stack: RAG, agents, MCP
  • Eval & observability pipeline
  • Code & runbook handoff
  • 4h SLA on business hours
3 to 9 months · Dedicated squad
Book intro call →
Operate

Sustained evolution in production.

Long-term partnership for AI systems already running. We keep them sharp, safe, cost-efficient and improving.
  • Monitored 24/7 operation
  • Continuous cost optimization
  • Model retraining & updates
  • Quarterly quality audit
  • Joint evolution roadmap
  • Compliance monitoring
12+ months · Embedded team
Talk to founder →
11 FAQ

Straight answers.

The questions we hear most
How is Daath.ai different from a Big Four AI practice?
Two things. First, the founder architects your system — you never get downgraded to junior consultants after the sale. Second, we ship production systems in weeks, not quarters, because we have no internal approval theater to run. Big Four still wins for global rollouts across 20+ regulatory regimes. For everything else, boutique specialists deploy 5–10× faster at a fraction of the cost.
Do you actually help with EU AI Act, LGPD or GDPR compliance?
Yes — as a design input, not an afterthought. We classify your use case by risk, document the model card, design the audit trail, and pick architectures that support explainability. We won't stamp legal opinions, but every system we ship is defensible in front of a DPO or regulator on day one.
Can you work with our existing data / ML team?
Preferred. Embedded engagement is our best mode — we sit inside your team, transfer patterns as we build, and leave your engineers stronger than we found them. Handoff is a success metric for us, not a fear.
How fast can you actually ship something to production?
A working prototype in 2–4 weeks. A production-hardened system with evals, observability and rollback in 8–16 weeks. Faster if you already have clean data and a real user, slower if we're also fixing the pipeline underneath. The Discovery sprint tells you exactly which case you're in.
What if we don't actually need AI for this?
We'll tell you. Roughly one in four Discovery engagements ends with «this should be a database query, a workflow rule, or a better UI, not an LLM». We'd rather bill four weeks and be honest than run a six-month project that doesn't move the needle.
Do you build with open-source, closed models, or both?
Whatever the problem needs. Frontier APIs (Anthropic, OpenAI, Google) where quality matters most. Open weights (Llama, Mistral, Qwen) where you need on-prem, custom fine-tuning, or cost control. Often a mix in the same architecture. The decision is data-driven, not ideological.
What about after the project ends?
Two options. Full handoff with a runbook and 30 days of on-call — your team owns everything. Or the Operate engagement: we stay lean and long-term, maintaining, tuning and evolving the system while your team focuses on product. Either way, no lock-in — everything runs on your infrastructure.
12 Contact

Got a hard problem?
Let's talk.

Reply within 48 business hours. A senior architect (usually the founder) joins from the very first call — no funnels, no pre-sales. Contractual confidentiality from the first contact.

calendly.com/roan-daath Book free assessment →