Localized 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.
Turning a design archive into a client-facing style system—not another generic image toy

CVision built a client-facing GenAI system for a branding studio—style families drawn from their archive, prompt and rule layers for consistency, and multi-round iteration so clients explore directions before designers burn time. Prototyping moved from days or weeks to a few hours, with auth, usage logging, cost tracking, and AWS hosting.
Creative studios and branding agencies with large design archives who want to accelerate prototyping, reduce early-stage workload, and let clients explore directions before designers commit fully to execution.
Infinity Design Studio had a strong portfolio and repeatable craft, but early-stage prototyping was the bottleneck: multiple first-draft concepts were still produced manually, clients needed several cycles to align on direction, and the founder was pulled into too many early decisions. Feedback often arrived late—after serious design effort was already spent.
The constraint was not quality. It was iteration speed and client alignment at the front of the funnel.
The team first framed the issue as “speed.” Under the hood, the audit showed something more useful:
The shift: the archive could become a reusable style system, not just a portfolio gallery.
CVision designed and deployed a client-facing GenAI prototyping system mapped to the studio’s real workflow—not a generic image generator with a new skin.
This shipped as a real internal and client-facing system—not a slide demo:
| Area | Before | After |
|---|---|---|
| Prototyping time | Days to weeks | A few hours |
| First concepts | Hand-built drafts, high manual load | Clients explore in-system; up to ~3 concepts per iteration, multiple rounds |
| Designer focus | Heavy early exploration | Refinement and craft after direction clarifies |
| Client feedback | Often late, after large effort | Clearer intent earlier in the process |
The studio moved from design-first iteration to system-assisted direction finding—a different operating model at the top of the funnel.
We map the workflow, then build the fastest version of your system—so talent is not wasted on process debt.
Explore Pilot Delivery LifelineShare this post
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.
Live copilot over Gmail, WhatsApp, Docs, and Notion—knowledge graph, daily priorities, and a real decision layer. Hours of context-building reduced to minutes.
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.
PROJECTFull neuromorphic SDK layer—PyTorch bridge, spiking/hybrid conversion, visualization, simulation, and observability—roughly six months faster than planned.
PROJECTAir-gapped, API-free legacy transformation pipeline—AI-assisted COBOL/C toward Java/Rust, controlled validation, modular design for regulated and mission-critical contexts.
PROJECTClient-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.
PROJECTCRM 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.
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.

Transformed chaotic Excel-based forecasting workflows into streamlined, automated systems, reducing processing time from one week to three hours.

PROJECTQualification 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.
PROJECTEircode-anchored hybrid pipeline: clean noisy input, deterministic parse, controlled LLM for ambiguity, confidence + review—~1000 addresses/day, accuracy from ~5–6% to over 80%.
Join established companies, from global brands to ambitious scaleups, who partner with us to achieve exceptional results.