GoFactAI
AI Workflow Automation

Automate
the work that matters.

FactAI Lab builds AI that extracts, routes, and processes high-value workflows, and keeps a record of what the AI did. Built for SaaS, real estate, and financial services. We start with a 30-day pilot on one real workflow and measure the result with you.

Run a property management office in BC? Meet Perch, our first product

SaaSCustomer success, contract review, usage analytics
Real EstateBC tenancy compliance, lease abstraction, property data
FinanceDocument processing, reporting, underwriting assist
Book a 20-Minute Discovery CallHow We Handle Your Data

No commitment required. 30-day automation pilot available.

Our first product: Perch, for BC property managers

Raising the rent, ending a tenancy or filling a home in BC? Start free.

Each free tool does one job from start to finish and emails you the result.

Free. No sign-up and no card. Each result comes to your email.

“If an agent decides to take a week off, or gets sick, the wave of emails does not stop, it only backs up,” writes a BC strata management firm.

Perch takes on that part: it drafts replies to tenant, owner and renter emails from your own inbox, with the BC rule and your property records beside it, and keeps track of every update you promised. Nothing goes out until you approve it. Demo by invitation.

See how Perch works

Perch is built by FactAI Lab in Vancouver.

Built for teams inFinancial ServicesLegalInsuranceHealthcareAsset Management

The automation gap is costing you

Your team spends hours every week processing documents and routing tasks that AI can help with. The tools exist. What's missing is a way to check what the AI did before it reaches your team.

1 workflow
chosen with you

We pick one document-heavy workflow, such as lease abstraction, loan underwriting or contract review, and automate it with AI extraction.

Baseline
measured first

We measure how often your current process gets it wrong before we build. Then AI outputs are checked against your source documents before they reach your team.

30 days
to a measured result

We instrument one real workflow, measure the impact with you, and aim to deliver a working automation in one month.

How we build automation that sticks

Not a chatbot layer. Not a prompt wrapper. Production automation infrastructure - with verification built in so your team can trust what ships.

◈

Document Intelligence

AI that reads your documents - leases, loan applications, contracts, statements - and extracts structured data at scale. From unstructured text to structured data your team can check.

Time saved, measured in your pilot
◉

Workflow Automation

Orchestrate multi-step processes: intake, triage, routing, approval, exception handling. Reduce manual handoffs, catch errors before they compound, and give your team time back for high-value work.

Labor saved, measured in your pilot
◎

Eval-First Quality

Every AI output is checked against your source documents before it reaches your team. We measure the error rate against your own baseline and keep a record of what the AI did for your legal and IT teams to review.

Error rate, measured against your baseline

How a pilot runs

We start from the tools you already use and agree each step with you.

01

Connect

We agree with you how one workflow connects to your documents and your existing model, and we start from the tools you already use. There is no software kit to install.

Connection agreed in the pilot scope.

02

Instrument

We place a check between the AI and your team that compares each output with your source documents, and we log the result.

Fallback or human review agreed per workflow.

03

Audit

Each interaction is logged: the prompt, the source passages used, the model, the output and the result of the check. The fields and the export format are agreed with you.

Record fields agreed in the pilot scope.

30-Day Automation Pilot

Ship one automation in 30 days

We identify your highest-ROI workflow, build the automation, measure the impact - and hand it over running. No six-month sales cycle. No PoC graveyard. If it doesn't reduce labor hours in 30 days, you owe nothing.

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Pilot Deliverables

  • ✓One production automation - live and processing real documents
  • ✓Baseline error rate measurement (before and after)
  • ✓Labor hours reclaimed - tracked over 30 days
  • ✓Data privacy and integration review for your stack
  • ✓Roadmap for 2–3 additional automation opportunities

What Clients and Collaborators Say

LinkedIn recommendations alongside anonymized project outcomes from client engagements.

LinkedIn recommendation from Wes Regan, PhD Candidate at UBC SCARP
LinkedIn recommendation from Keyi Tang, Staff Applied Scientist at DoorDash
LinkedIn recommendation from Alexey Iskrov, Engineering Leader
LinkedIn recommendation from Amir Ghaffari, CFO

AI extraction pipeline reduced loan document review from 3 days to 4 hours.

VP Operations · BC Credit Union

RAG system resolved 70% of tier-1 support tickets without escalation. ROI positive in month two.

CTO · Series A SaaS

AI audit framework surfaced compliance gaps missed by the internal team across 3 review cycles.

Chief Risk Officer · Insurance Firm

Our methodology

The SAFE Framework

How we approach every AI deployment - from first conversation to production monitoring.

SScope

Define which workflows need AI and which don't. Not every process benefits from automation - the ones that do need a clear brief before a line of code is written.

AAudit

Instrument every AI output with verification scores, source grounding, and immutable logs. Every decision becomes traceable before it reaches a user or examiner.

FFit

Match the right approach to each workflow - LLM, RAG, agents, or classical ML. The model that fits earns its place. The one that doesn't gets ruled out early.

EEval

Evaluation-first deployment. Measure hallucination rate, latency, and accuracy before shipping. Monitor the same metrics after launch. Ship when the numbers earn it.

Every FactAI Lab engagement follows this framework.

Pedram Agand, Founder of FactAI Lab
Pedram Agand
Founder, FactAI Lab — SFU & AI Research Background
·AI systems researcher with focus on reliability and verification
·Published work at IROS, NeurIPS workshops
·Built and deployed LLM systems in regulated contexts
·Direct background in compliance-adjacent AI applications
Research profile →
Why FactAI Lab

Built by someone who has sat in your compliance team's chair

I didn't build FactAI Lab because AI is trending. I built it after watching compliance teams at financial firms reject valid LLM use cases because they couldn't explain the outputs to examiners.

The technology to verify AI outputs exists. The infrastructure to make that verification audit-ready - that's what FactAI Lab provides. Your AI investment should not be a liability. It should be defensible.

Talk to the Founder

Common questions

Free Guide

16 AI workflows with proven ROI for modern operators

Sixteen production-tested automations - each with the failure mode it prevents and the metric that proves it works.

Tech & SaaS (5 workflows)Financial Services (5 workflows)Cross-Vertical Infrastructure (3 workflows)Legal & Operations (2 workflows)Healthcare (1 workflow)
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Ready to make your AI defensible?

20 minutes. No slides. We talk about your specific workflows, your regulatory environment, and whether FactAI Lab is the right fit.

Responding to all inquiries within 24 hours.

Program PartnersMicrosoft for StartupsAWS for StartupsChang School at SFUL2M Accelerator