AI-powered software development services

LLM applications, retrieval-augmented generation, fine-tuning, and classical ML where it still wins.

We build LLM-powered products that survive production: structured prompts under version control, retrieval pipelines you can reason about, and evaluation harnesses that catch regression before customers do.

We are vendor-pragmatic — OpenAI, Anthropic, Google, Mistral, open-weight models on your own infrastructure — and we will tell you when a smaller fine-tuned model beats a frontier model on cost and latency.

Agentic workflows and copilots that automate real operational work, not chatbot demos.

Most agent projects fail because the agent loop is wide open. We constrain tool-use, build the evaluation suite first, and ship a narrow agent that does one job well before broadening it.

We integrate with your existing systems — Slack, Linear, Salesforce, custom ERPs — and instrument every step so you can audit what the agent did and why.

Modern web applications in React, Next.js, and TypeScript, engineered to host AI features without rotting.

We ship typed Next.js applications on Vercel, AWS, or your hosting of choice. Streaming UIs for LLM responses, edge runtime for low-latency completions, and the boring engineering hygiene that lets the AI parts evolve quickly.

Where appropriate we also work in Astro, SvelteKit, and Remix — we pick the framework based on the problem, not the trend cycle.

React Native and Flutter apps with on-device AI where it makes sense.

Cross-platform mobile apps that share business logic across iOS and Android, with native modules where the platform demands it. On-device inference using Core ML, TensorFlow Lite, and ONNX Runtime for latency-sensitive features.

Release pipelines, crash reporting, and over-the-air updates are part of the deliverable — not a follow-up engagement.

AWS, Azure, and GCP infrastructure for AI workloads, with the observability the rest of your team will need.

We design cloud architectures that price out at scale — GPU pools you can actually use, batch pipelines that don't time out, model registries that survive a team change.

MLOps with us means evaluation gates in CI, drift monitoring in production, and rollback paths that take minutes, not days.

Authentication, prompt injection defence, data isolation, and the audit trails enterprise customers ask for.

Security on AI products is not optional — and it is not just OWASP. We build prompt-injection defences, data exfiltration controls, and per-tenant isolation that holds up under penetration testing.

We can map your AI features to NIST AI RMF, ISO/IEC 42001, and EU AI Act categories so your enterprise sales team has the answers they need before procurement asks.

Data engineering for AI: SQL, NoSQL, and vector stores, with the indexing and chunking strategy your retrieval actually needs.

A bad chunking strategy will sink the smartest LLM. We design ingestion pipelines that preserve structure, embed at the right granularity, and re-index when source data changes — not on a schedule, on an event.

We work with pgvector, Pinecone, Qdrant, Weaviate, and Vespa, and we will recommend whichever fits your scale, latency, and operations budget.

REST, GraphQL, and LLM API integrations that age well — typed clients, rate-limit aware, observable.

We ship typed API clients with retry-and-backoff that respects vendor rate limits, structured logging that survives a SEV-1 post-mortem, and SDKs your other teams can adopt without a workshop.

For LLM API work specifically: streaming, tool-calling, structured output, cost accounting per request, and the fallback layers that keep your product up when a vendor has an incident.