CogniForge AI · Vancouver
Est. 2024
From our West Georgia studio, CogniForge AI designs and ships custom assistants, agents and workflow automation grounded in your documents and know-how — retrieval-augmented generation, knowledge bases and guardrails, with humans kept in the review loop. Accuracy depends on your data and scope; it is never guaranteed.
Knowledge-driven AI studio · BN 917452683 BC0001 · Vancouver
What we are — and are not
CogniForge AI is an applied-AI consultancy and custom build studio serving Canadian businesses, SMEs, scale-ups and enterprise teams. We design and ship generative-AI applications, large language model (LLM) assistants, AI agents, machine learning models, data pipelines and retrieval-augmented generation (RAG) systems that turn scattered documents into dependable, source-cited answers. We are a professional services firm — not a course, not an AI-income scheme, not a wellness brand because of our .life domain, and not a blacksmithing shop because of our name.
Most organizations we meet carry critical know-how in PDFs, wikis, ticket threads and the heads of three senior people. When a new hire asks a reasonable question, someone stops their real work to answer it again. When a client needs a policy detail, staff grep folders and hope they found the right version. Our forge method addresses that friction: we audit what you have, design a grounding pipeline, build a prototype you can test against real questions, then harden evaluation harnesses and MLOps before production deployment. Every engagement keeps human review in the loop — because even well-built retrieval systems can miss context, cite the wrong passage or hallucinate when sources are thin.
We work from downtown Vancouver but serve clients across the Canadian market. Project fees are quoted in CAD. Typical engagements begin with a discovery sprint, move through a proof of concept, and scale to retainer support when you need ongoing model evaluation, prompt engineering refinement and API integration with your existing tools.
Illustrative reach
40+
Knowledge systems scoped for Canadian clients
6–10
Weeks from discovery sprint to working prototype
C$85k
Typical starting range for production RAG builds
Figures are illustrative of past project scope — not a promise of timeline, cost or outcome for your engagement.
The forge method
01 — Gather
We inventory your sources — policies, manuals, CRM notes, tickets — and map what should be retrievable versus what must stay human-only. NLP and document intelligence tools help, but judgment calls stay with your team and ours.
02 — Ground
Chunking strategy, embedding choices, metadata filters and hallucination guardrails are designed for your domain. We prototype retrieval-augmented generation pipelines and measure recall before anyone trusts the answers.
03 — Forge
AI assistants and agents take shape with prompt engineering, fine-tuning where justified, and workflow automation hooks into the tools your staff already use. Computer vision or custom ML models join when the problem demands it.
04 — Ship
Model evaluation, responsible AI review, MLOps and PIPEDA-compliant data handling carry the system into production deployment. Retainer support keeps guardrails current as your documents change.
Capabilities
Roadmaps, use-case prioritization and discovery sprints that separate retrieval wins from automation theatre.
End-to-end retrieval systems with source-cited answers, access control and evaluation harnesses.
Extraction, classification and structuring of messy document corpora for downstream ML and LLM use.
AI assistants that cite sources, plus agent orchestration for multi-step workflows with human checkpoints.
API integration with CRMs, ticketing, ERP and internal tools — dependable knowledge automation, not brittle scripts.
Guardrails, regression tests, monitoring and responsible-AI practices for models in production.
Selected work
Canadian logistics scale-up
A BC-based logistics scale-up maintained routing rules across twelve PDF versions and a wiki nobody trusted. We built a RAG assistant that returns source-cited answers with page references, integrated into Slack. Escalation paths stayed human — the system flags low-confidence retrievals rather than guessing. Past metrics showed faster onboarding; your results will depend on document quality and adoption.
National retailer · document intelligence
Contract clauses buried in scanned agreements were blocking procurement reviews. Our document intelligence pipeline extracts structured fields, routes exceptions to senior reviewers, and feeds a knowledge base the legal team can query. Machine learning models handle layout variation; humans approve edge cases.
Tell us what your team searches for every week. We will scope a discovery sprint, quote CAD project fees honestly, and forge a proof of concept you can evaluate before committing to production deployment.
Forge a prototypeLimits & scope: We build document-grounded assistants, retrieval pipelines and automation for client teams — never off-the-shelf training or income products. Language models can mis-cite, skip nuance or sound right while wrong; every deployment includes human review paths. Budget, data quality and adoption shape outcomes; we do not contract for guaranteed ROI, flawless retrieval or error-free production. Work samples show what shipped before, not what your project will achieve. Engineering consultancy only — not legal, tax, investment or wellness advice.