Which LLM providers do you work with?

We build against whichever provider fits the project's requirements and budget, and design integrations so the underlying model can be swapped without rewriting the surrounding application.

Can you add AI features to an app that already exists?

Yes — this is most of our AI development work. We integrate chat, retrieval, or agent features into an existing codebase without requiring a rewrite.

How do you handle AI output reliability?

We validate structured outputs against a schema (typically Zod), design fallback behavior for malformed or low-confidence responses, and avoid presenting unvalidated model output directly as fact where it matters.

AI Development

We build AI-powered features on top of your existing product or as new AI-native applications — LLM integrations, retrieval-augmented generation, agents, and structured-output workflows — using the same engineering discipline as any other production system.

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Common problems

AI features get prototyped in a notebook and never make it into a production-grade application.
Retrieval systems return irrelevant context because chunking and indexing weren't designed for the actual content.
LLM outputs aren't validated, so malformed responses break the UI or downstream logic.
Token costs and latency aren't accounted for until the bill arrives.

What you get

LLM integrations built with typed, validated inputs and outputs — not raw string parsing.
Retrieval pipelines tuned to your actual content and query patterns, not a generic template.
Agent workflows scoped with clear guardrails instead of open-ended tool access.
Cost and latency budgets designed in from the start, with caching and model selection to match.

Capabilities

What our ai development work covers

LLM integration

Chat interfaces, structured-output extraction, and streaming responses integrated into existing applications.

Retrieval-augmented generation

Chunking, embedding, and retrieval pipelines tuned to your content and query patterns.

Agents & tool use

Scoped agent workflows with explicit tool permissions and guardrails, not unrestricted autonomy.

Model Context Protocol

MCP server and client integrations connecting AI features to your existing tools and data.

Technology

Stack we build with

LLM APIsVector databasesModel Context ProtocolNext.js / Node.jsZod

Project types

AI chat and copilot features added to existing apps
Retrieval-augmented knowledge base tools
AI SaaS products
Internal AI-assisted workflow automation

Deliverables

Production-grade LLM integration with validated outputs
Retrieval pipeline tuned to your content
Cost and latency budget with model-selection rationale
Evaluation approach for AI feature quality

Related services

SaaS DevelopmentMulti-tenant products from architecture to billing.

Next.js DevelopmentServer-rendered, SEO-friendly apps built on the App Router.

TypeScript DevelopmentType-safe codebases that scale with your team.

FAQ

Frequently asked questions

Ready to talk about ai development?

Tell us about your goals, timeline, and budget. We'll reply with next steps.

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