Mission Control · AI Product Studio · Canada

Automate the Ordinary, Spark the Extraordinary

Enterprise-born. Lab-built. SphotaLabs is the launchpad — we design, build, and launch AI products that simplify work at scale.

Our story
Canada · Enterprise AI

We cut the work
what's that slows growth.

We build AI that turns repetitive, manual work into fast, compliant action. SphotaLabs helps SMBs and enterprise teams process documents, photos, video, and IoT data without adding headcount, while keeping privacy, security, and data sovereignty intact. We also customize integrations with the systems you already use so adoption is faster and the value shows up sooner.

CA
Built and hosted in Canada
3
Products launched for enterprises
0
Extra headcount required to start

Why we exist

Too many teams still lose hours every week re-keying data, chasing approvals, and searching through files that should already be usable. We built SphotaLabs to remove that waste, reduce operating cost, and give lean teams the leverage of software instead of more payroll.

How we deliver value

Our platform ingests documents, images, video, and sensor data, applies AI safely, and stores outputs in a legally compliant, data-sovereign, privacy-first way. That means faster turnaround, fewer manual errors, lower processing costs, and a platform that can scale with your business and pass regulated-market scrutiny.

🍁

Built for Canada. Ready for regulated markets.

SphotaLabs is focused on Canadian businesses first, with products built for teams that need practical AI now. For SMBs, that means doing more work with the same team, avoiding costly manual processing, and keeping sensitive data in the right place. Our technologies are pending patents and trademarks in multiple jurisdictions, and we are working toward independent third-party certification and published results.

AI Products in Orbit

SphotaLabs is the launchpad. Each product is built in the lab and launched into orbit.

SAT-01In Orbit
DocuParser.io

DocuParser.io

AI-native document processing

AI-powered data extraction for accounting, FP&A, insurance, logistics, and healthcare teams. Eliminate manual re-entry by parsing, classifying, and extracting fields from invoices, contracts, and supporting documents. All data stored and processed in Canada.

SAT-02On the Launch Pad
ReasonLedger

ReasonLedger

Record reasons. Build better AI.

A developer-first warrant ledger for AI-assisted decisions. Capture, document, and audit the reasoning behind every AI-driven choice — via CLI, SDK, or VS Code extension. Local-first, privacy-respecting, and built for teams that need accountability in production AI workflows.

SAT-03In the Lab
CovenantIQ

CovenantIQ

Agreement intelligence for telecom & TMF

An AI-native agreements platform for telecom and TM Forum (TMF)–aligned APIs. Parse agreements, surface key clauses, monitor commitments, and keep obligations searchable. In development and seeking design partners.

What powers our products

From complexity to simplicity

We build on a shared foundation: precise document intelligence, data stored and processed in Canada, SOC 2-compliant cloud infrastructure, and PIPEDA-aligned privacy—designed for enterprises and regulated environments. Our goal is to simplify complex processes so teams can focus on what matters.

Multimodal data extraction & classification

Extract line items, entities, signals, and key fields from documents, photos, videos, and IoT sensor data so your teams can trust the inputs they use to make decisions, close faster, and reduce manual work.

Stored and processed in Canada

Customer documents are uploaded, extracted, and inferred in Canada—built for public sector, government, and regulated industries that require in-country processing.

Enterprise security

Built on SOC 2-compliant cloud infrastructure with encryption in transit and at rest, plus access controls suited to enterprise and regulated workloads.

Privacy by design

PIPEDA-aligned privacy practices reflected in our policies and product design—so teams can adopt DocuParser with clear expectations on how data is handled.