We don’t just consult. We ship working software. UNVEIL designs and builds full-stack web, data, and document applications with AI capabilities engineered into the product from day one — not added as an afterthought. You get a system you can run, demonstrate, and grow.
Every application we build sits on open-source foundations so the client can audit any layer, swap any component, and self-host where required. No proprietary middleware. No vendor lock-in. The source code is yours.
What we build
- Web and SaaS applications — modern stacks (React / Vite / TypeScript / Tailwind on the front end, Python / FastAPI or Node on the back end) shipping fast, accessible, mobile-friendly, and PWA-capable.
- Document and data platforms — upload, extract, search, review, and route. Document understanding (OCR, layout, tables), hybrid full-text + semantic search, structured-data export, and API surfaces ready for downstream consumers.
- Agentic and copilot interfaces — chat-driven workflows backed by retrieval-augmented generation, structured tool use, and audit-ready event logs. Long-form, context-aware conversations grounded in your data.
- Internal tools and operational dashboards — bring AI directly to the analysts, operators, claims reviewers, eligibility specialists, and subject-matter experts who do the work.
- Mobile-friendly capture and review — camera-driven scanning, photo upload with auto-capture, batch review queues. We’ve shipped iPad-class capture frontends with sub-second feedback loops.
- APIs, integrations, and embedded models — drop-in services that other systems can call, including integrations with cloud-native AI services and self-hosted model servers.
How we build
- Production-grade from sprint one. Containerized, reproducible from a clean clone. Docker Compose is the source of truth for development; cloud or on-prem deploy from the same definition.
- Type-safe end to end. TypeScript on the front end, type-hinted Python on the back end, schema-validated API contracts in between.
- Async by default. Modern async backends so the system stays responsive under real workload.
- Tested where it matters. Unit tests on critical paths, integration tests on workflows, smoke tests for deploys. We don’t theatre-test for coverage numbers.
- Audit-ready. Append-only audit logs, PII scanning, structured event streams. Designed for FOIA / public-records and regulated environments rather than retrofitted later.
- Open-source by preference. PostgreSQL with vector and full-text extensions. S3-compatible object storage. Standard LLM provider APIs. Components you can replace.
Use cases / real-world applications
- AI-native product MVPs — go from concept to working software inside a single sprint cycle, with stakeholder-ready demos.
- Document-heavy operational software — capture, extract, classify, route, and search. Searchable PDFs, structured exports, hybrid retrieval.
- Decision-support copilots — embed retrieval and reasoning into the workflows your team already uses.
- Customer-facing chat experiences — long-form conversational AI grounded in private corpora, with citation-back-to-source guarantees.
- Internal analyst platforms — wrap your data warehouse with task-shaped AI tools your operators actually use.
- On-prem and air-gapped deployments — deliver AI software for clients who cannot or will not send data to a public cloud.
Why this matters
Most AI engagements stall at the slide-deck stage because the buyer ends up with a strategy memo and no software. Most “AI-powered” apps from generic dev shops bolt a single LLM call onto a stale CRUD app. We sit in between: a small senior team that designs the AI capability and ships the production application that delivers it.
Next steps
Have an idea you need built — or an existing system that needs AI engineered into it the right way? Contact us today to schedule a free consultation and walk through scope, stack, and the fastest credible path to shipping.
