Technology
We're intentionally model- and cloud-agnostic. Here's the toolkit we reach for — and how we choose per project.
We're model-agnostic. Every project picks the best model per task based on latency, cost, evaluation scores and data-residency needs — not vendor lock-in.
Start simple, scale when the workload demands it. Postgres + pgvector covers most enterprise RAG cheaply; specialised stores step in for high-cardinality, high-QPS or geo-replicated workloads.
We favour thin, testable orchestration over heavyweight abstractions. Frameworks accelerate the 80% — we always own the critical paths in plain code.
Deploy where your data lives. Multi-cloud experience means we can bring workloads to your existing spend commitments, or run entirely inside your VPC.
Great AI needs a solid data foundation. We invest early in schema, observability and lineage — not just prompts.
Type-safe, accessible, fast. Shipping AI features that people actually use requires product-quality UX — streaming responses, optimistic UI, empty and error states.
Voice and document AI are where LLMs meet the physical world. We combine best-in-class ASR, TTS and OCR with domain-specific post-processing.
You cannot ship what you cannot measure. Every T7 AI system ships with automated evals, request-level traces and drift monitors from day one.
Enterprise deployments need guardrails, audit trails and policy-based access — built into the app, not bolted on.
We adapt to yours. Tell us what you're running and we'll show you how we'd fit in.
Talk architecture with us