PROJECTLocalized Creative Hiring MVP Built in Two Weeks
Sanjh: full-stack marketplace MVP for Pakistan—talent and vendor flows, AI-assisted matching, admin, deployable in ~2 weeks. Structure over bloat, validation over feature creep.
PROJECTAI Copilot for a Unified Business Decision Layer
Replacing morning reconstruction with a self-updating layer that actually knows your business
CVision built an AI copilot that replaces morning reconstruction with a live decision layer—ingesting Gmail, WhatsApp, Google Docs, and Notion into a knowledge graph with daily summaries, prioritized actions, and a centralized dashboard. Deployed for 1+ years as the operational brain of a real business, not a chat wrapper.
Meet our client
Client: BridgeBot
Industry: AI copilot, knowledge systems
Market: Global
Solutions: Pilot Delivery Lifeline
Who this is for
Founders and operators juggling multiple workflows, teams, or business units where information lives in too many places—so decisions depend on manually stitching context, and planning eats the day before work begins.
The challenge
The day did not start with execution. It started with reconstruction: scanning Gmail, WhatsApp, Notion, Google Docs, and mentally piecing together what mattered and what had to happen next.
That was not a tooling gap in the narrow sense—it was a context problem.
- Hours lost daily just understanding the state of the business.
- Fragmented knowledge across platforms.
- Missed links between conversations, ideas, and action items.
- Rising cognitive load as complexity grew.
What CVision identified
The first ask sounded small: track action items better. That was not the root issue.
CVision identified a missing unified decision layer: data existed, but nothing connected it reliably. The founder was effectively acting as the glue between systems.
The answer was not a feature—it was a living system that continuously understands the business.
Team collaboration and operations
What we built
CVision designed and deployed an AI copilot layer for the business— BridgeBot—as a continuously updating system.
It ingests from:
- Gmail
- Google Docs
- Notion
…structures that feed into a knowledge graph, connects conversations, documents, and ongoing work, and generates usable outputs in near real time.
System capabilities
- Daily summaries
- Prioritized action items
- Contextual insights
- Alerts on important developments
- A centralized dashboard view
Intelligence layer
This is intentionally not generic summarization. The system connects ideas across channels, detects patterns in communication, tracks threads, proposes actionable next steps, and maintains continuity over time.
Why off-the-shelf tools could not substitute
ChatGPT, Notion AI, Zapier, or lightweight automation each operate in isolated contexts. They do not maintain a persistent, evolving model of a whole business across live streams.
BridgeBot was aimed to be live, self-updating, context-persistent, and cross-platform by design—a different class of system than a wrapper on a single API.
Production reality
- Deployed and actively used for more than a year.
- Integrated across multiple live data sources.
- Used daily as a decision layer—not an experiment.
- Fully documented and transferable.
- Includes observability and transparency into system behavior.
Outcome
| Dimension | Before | After |
|---|---|---|
| Planning | Hours reconstructing context | Minutes with AI-prioritized execution |
| Visibility | Manual scanning across tools | Single dashboard view of what matters |
| Decisions | Often reactive | Context delivered proactively |
- Reduced daily planning from hours to minutes.
- Removed manual context-building as a daily ritual.
- Improved clarity and decision speed.
- Lower cognitive overhead as the business scaled in complexity.
Knowledge fragmented across tools and conversations?
Book an AI workflow audit and we will map how your setup can become a unified decision layer.
Book an AI workflow auditShare this post
Showcasing Our State-of-the-Practice
View Our ProjectsPROJECTPROJECTAI Copilot for a Unified Business Decision Layer
Live copilot over Gmail, WhatsApp, Docs, and Notion—knowledge graph, daily priorities, and a real decision layer. Hours of context-building reduced to minutes.
PROJECTStructured AI for Startup Quality and Investor Readiness
Full-stack AI product: structured founder journeys, LLM critique of decks, iteration loops, progression tracking—Next.js, Node, PostgreSQL. From manual reviews to scalable founder development.
PROJECTNeuromorphic SDK Six Months Ahead of Schedule
Full neuromorphic SDK layer—PyTorch bridge, spiking/hybrid conversion, visualization, simulation, and observability—roughly six months faster than planned.
PROJECTSecure AI for Legacy Code in Regulated Environments
Air-gapped, API-free legacy transformation pipeline—AI-assisted COBOL/C toward Java/Rust, controlled validation, modular design for regulated and mission-critical contexts.
PROJECTFrom Weeks to Hours: GenAI Logo Prototyping
Client-facing GenAI for logo concepts—studio style families, guided prompts, multi-round iteration, auth and cost tracking on AWS. Designers focus on refinement, not endless first drafts.
PROJECTFrom Messy CRM to Daily AI Qualification
CRM cleanup on HubSpot/GHL, then daily AI calling with qualification, booking, funnel routing, and Sheets reporting—~10–15 hours saved per week for a solo operator.
PROJECTProduction AI Receptionist Platform
Multi-tenant voice platform in ~2 weeks: VAPI + telephony, tasks and transcripts, WhatsApp to owners, human fallback, and a Next.js dashboard—built for real operators, not engineers.
ConfidentialPROJECT
Data Modernization for a Global FMCG Leader
Transformed chaotic Excel-based forecasting workflows into streamlined, automated systems, reducing processing time from one week to three hours.
PROJECTFranchise Lead Qualification at Scale
Qualification logic on top of existing CRMs: MCP-powered access, semantic and rule-based scoring, daily top-lead digests, and AI-assisted outreach—$373K–800K franchise commitment.
PROJECTHybrid Address Intelligence: From ~5% to 80%+ Accuracy
Eircode-anchored hybrid pipeline: clean noisy input, deterministic parse, controlled LLM for ambiguity, confidence + review—~1000 addresses/day, accuracy from ~5–6% to over 80%.
Unlock a Competitive Edge With AI Solutions
Join established companies, from global brands to ambitious scaleups, who partner with us to achieve exceptional results.