Data Forge is a Canadian applied-AI and data-engineering studio on Howe Street in Vancouver. We design data pipelines, warehouse models, analytics dashboards and AI-ready retrieval foundations — including retrieval-augmented generation (RAG) indexes — for organizations who need a single source of truth, not another spreadsheet that three departments interpret differently. We are a professional AI consultancy and data foundry. We are not a course, not an AI income scheme, and not a data broker that buys or sells your customer records.
The foundry
Most teams we meet carry the same quiet headache: a forecast nobody on the finance team actually trusts, three tools that disagree about the same customer, a data warehouse everyone is a little afraid to query. Data Forge exists to take that raw, scattered material and forge it into pipelines, governed models and decision dashboards your operators can rely on — with human-in-the-loop review where stakes are high.
We work across data engineering, predictive analytics, machine learning foundations, generative AI data layers and MLOps for data systems. Our Vancouver studio serves Canadian startups, scale-ups and enterprise teams who need production-grade data infrastructure — not a proof of concept that stalls when upstream schemas change. We quote project scope in CAD, document assumptions honestly, and tell you when your source data is too thin for the model you are asking for.
The domain dataforge.life is branding only. We do not sell lifestyle tracking, wellness metrics or a quantified-self product. We engineer your data under PIPEDA-aligned governance — with data contracts, quality checks and drift monitoring that keep retrieval indexes and dashboards honest after deployment.
Figures reflect past project scope — not a promise of timeline, cost or measurable outcomes for your engagement.
Foundry method
Our client engagement follows a deliberate sequence — no mystery phases, no vendor theatre. Each pillar has deliverables you can inspect before we advance to the next stage of the build.
We audit source systems, define data contracts and document what enters your pipeline — batch and streaming. Discovery separates signal from folklore in your exports and API feeds.
Schema design, deduplication, validation rules and governance guardrails. Data quality checks run before anything reaches a warehouse model or retrieval index.
Warehouse layers, semantic models and AI-ready embeddings. We prepare foundations for machine learning, large language models (LLMs) and natural language processing (NLP) workloads.
Decision dashboards, API integration, automation hooks and monitoring. DataOps keeps pipelines observable; humans stay accountable for high-stakes calls.
How we work
Every engagement starts with a scoped discovery sprint or data assessment — stakeholder interviews, source-system mapping, architecture options and a written roadmap. You receive working pipelines, warehouse models or dashboards depending on project scope. When systems are live, many clients move to a retainer for DataOps, drift monitoring and responsible-AI governance updates.
We are an AI studio focused on custom delivery for Canadian organizations. The people who scope your build are the people who answer the pager when a late-night batch job misbehaves. That continuity matters when your retrieval-augmented generation layer depends on yesterday's ingest completing cleanly.
Pipeline review · Howe Street studio
Schema design · refined data seam
Capabilities
Selected work
Anonymised illustrations of past engagements — metrics depend on data quality, scope and adoption; not promises for your project.
A Canadian logistics scale-up had dispatch, billing and maintenance data in three systems that never agreed. We built batch and streaming pipelines into a governed warehouse, with data quality checks and a decision dashboard ops managers actually opened each morning. A human still signs off on exception routes — automation handles the routine.
Read the case →A national retailer's support team drowned in contradictory policy documents. We forged a retrieval index with chunking, metadata contracts and evaluation harnesses for their LLM application — with prompt engineering guardrails and a human review step for refund edge cases. Inference latency and citation accuracy improved; we do not guarantee similar outcomes for every corpus.
Read the case →Common questions
No on all counts. Data Forge is a data-engineering and analytics studio that builds pipelines, warehouses, dashboards and AI-ready foundations for client organizations. The .life TLD is branding only — not a lifestyle, wellness or "data about your life" service. We do not buy or sell data; we engineer your own data. We do not guarantee specific accuracy, cost savings or outcomes; those depend on your data quality, scope and infrastructure, and a human stays in the loop.
Most relationships begin with a fixed-scope data assessment or discovery sprint. You receive a written roadmap and working deliverables depending on scope. When pipelines are live, many clients move to a monthly retainer for DataOps, monitoring and governance — defined hours, not an open-ended feature factory.
Indicative ranges only: data assessments C$18,000–C$32,000; warehouse and pipeline builds C$75,000–C$220,000; AI-ready retrieval foundations from C$55,000. Retainers often start around C$6,500/month for DataOps support. Final quotes depend on source complexity and integration depth.
Start here
Tell us about the warehouse nobody trusts, the dashboard that reports last quarter's truth, or the retrieval layer your LLM application needs. We respond within one business day.