Retrieval-Augmented Generation (RAG)
Vector-backed knowledge systems grounded in your documents, tickets, and databases. Faithfulness evals, citation rendering, and hallucination monitoring so answers stay trustworthy past the demo.
Services
Engineering that earns trust.
Four practice areas, one principle: deliver work that survives real-world use. Here's where we dig in.
AI Engineering
Production AI — not demos. We ship RAG, agent workflows, and domain copilots with the evals, tracing, and guardrails that keep them reliable after launch.
Agents & Workflows
Multi-step agents that execute real work — not chat toys. Tool orchestration, tracing, cost and latency SLOs, and guardrails that keep them from going off the rails in production.
Domain Copilots
Embedded assistants for support, sales, and engineering productivity. Our own site's chatbot is a live example — ask it anything about SBSNext.
Try the chatbotTypical engagement
A 10–14 week pilot that replaces one real workflow — measurable outcome, production deploy, knowledge transfer. Not a slideware strategy deck.
Test Automation
Playwright-first test systems engineered for reliability at scale. Self-healing locators, AI-generated cases, sandboxed per-commit runs — so tests stop being the thing that slows you down.
Playwright Architecture
Accessibility-tree locators, component-level isolation, and flaky-test detection baked in. We design frameworks any engineer on your team can extend without calling us back.
AI-Generated Test Cases
Read a user story, generate permutation-rich test cases, push them straight into Azure DevOps or Jira. We built this pattern for a biBERK (Berkshire Hathaway) rating engine: 200+ permutations across 20+ API calls, all automated.
Sandboxed Per-Commit Testing
Containerized application-under-test runs locally and on every commit. Developers get real feedback before merge — not a two-day wait for a flaky CI suite.
API-Heavy Testing Pyramid
Broad API coverage at lower cost than UI testing. Other test strategies reuse the data setup for seamless integration and less duplication.
Performance & Load
JMeter and Artillery with automated baselines and anomaly detection. Critical regressions block the release; noise gets handled at configurable thresholds.
Turnarounds
We've taken flaky, distrusted automation suites and rebuilt them into release-blocking systems teams rely on — including NBA streaming tests and insurance-domain UI portals.
Full-Stack Development
Next.js 15, React 19, TypeScript end-to-end, edge deploys on Vercel or Cloudflare. Clean architecture your team will want to keep working in — not a handoff that rots in six months.
Modern Web & APIs
Next.js App Router with React Server Components, tRPC for type-safe APIs, Drizzle for the data layer. Server components where they help, client where they're needed — not cargo-culted.
Edge & Cloud
Deploy to Vercel, Cloudflare Workers, AWS, or Azure. We choose based on your latency, cost, and compliance constraints — not because we're certified in one.
Enterprise Backends
C# / .NET and Python when the workload calls for it. IaC'd pipelines in Azure DevOps or GitHub Actions, Kubernetes when it's the right tool, not before.
Codebases that age well
Typed end-to-end, tested, documented where it matters. We leave your team with something they can extend without us — that's the point.
AI Agent Enablement
Your team is using AI coding tools and getting generic output. We turn those tools into senior teammates — agents that know your codebase, your standards, and your workflow, so your engineers move 5–10x faster.
Agent Architecture Audit
We review how your team actually uses AI coding tools today — where they help, where they hallucinate, where engineers have given up. You leave with a concrete roadmap, not a framework diagram.
Custom Agents for Your Workflow
Review agents, migration agents, test generators, deployment assistants — each tuned to your stack, your conventions, and the specific problems your team hits weekly. No generic prompts.
Guardrails & Context Engineering
The difference between a useful agent and a dangerous one is context and constraints. We design both — codebase maps the AI actually reads, pre-action checks, post-action verifications.
Tool Integration
Connect AI tools to Jira, Postgres, observability stacks, your internal APIs. Context is the multiplier — an agent with access to your real data stops guessing and starts executing.
Team Enablement
We don't just build the agents and disappear. Your engineers learn to author, debug, and evolve their own — so the system compounds after we're gone.
Proof of concept
This website — copy, components, chatbot, worker, deploys — was built end-to-end using the same patterns we deploy for clients. We use what we sell.
Ready to build something?
Tell us about your problem. We'll tell you honestly whether we're the right fit.