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.
