Custom AI Development
When off-the-shelf AI doesn't fit the problem, we build it from scratch — LLM-powered applications, document intelligence pipelines, semantic search, domain-tuned models, and AI features embedded into your existing product. Production-grade engineering, not notebook demos: evaluation, monitoring, cost controls, and full documentation.
Kinds of custom AI applications we've shipped
Most engagements blend two or three of these. What unites them: production-grade engineering, not proofs-of-concept.
LLM-Powered Applications
End-user-facing apps built around large language models — chat interfaces, domain experts, copilots, guided workflows. We handle the model selection, prompting, evaluation, and production infrastructure.
Examples: Internal legal Q&A assistant · Customer-facing product advisor · Clinical documentation copilot
Document Intelligence
AI systems that read, understand, and act on documents at scale. Classification, extraction, summarization, compliance checking, Q&A over your own corpus.
Examples: Contract review tool · Policy Q&A over internal docs · Compliance-check pipeline
Semantic Search & RAG
Search systems that understand meaning, not just keywords. Retrieval-augmented generation grounded in your content — accurate answers with citations.
Examples: Internal knowledge base search · Support content grounding · Multi-source research tool
AI Features for Existing Products
AI capabilities embedded into your existing SaaS, app, or internal tool. We integrate with your codebase and ship features your users can use immediately.
Examples: AI-powered summaries in your dashboard · Smart autocomplete in your editor · Predictive fields in your forms
Domain-Tuned Models
Fine-tuned or RAG-tuned models specialized on your domain, terminology, and data. For cases where general-purpose models aren't accurate or consistent enough.
Examples: Industry-specific classifier · Domain-tuned response generator · Compliance-aware summarizer
Custom AI Infrastructure
Production-grade infrastructure for running AI in your business — evaluation, monitoring, prompt/version management, cost controls, access governance.
Examples: Prompt registry · LLM observability pipeline · Multi-tenant AI governance layer
When to build custom AI
Not every AI problem needs a custom build. Here's when it does.
You need more than a wrapper around ChatGPT
Your use case has edge cases, compliance requirements, proprietary data, or UX expectations that off-the-shelf tools can't meet.
You have proprietary data that creates unique value
Your customer data, content library, historical records, or domain expertise is your moat. Custom AI puts it to work without leaking it.
AI is a feature, not the product
You're embedding AI into an existing SaaS, internal tool, or workflow. You don't need a standalone AI platform — you need capabilities shipped into what you already have.
Reliability and evaluation matter
A demo that works in a notebook isn't production-ready. You need evaluation, monitoring, cost controls, and governance before you can safely ship.
What we build with
We pick the right model, framework, and infrastructure for the job — not for the marketing deck.
OpenAI (GPT-5, GPT-4o), Anthropic (Claude 4.6, 4.5), Google (Gemini), Mistral, Meta (Llama), and open-weight models as appropriate
LangChain, LangGraph, LlamaIndex, Vercel AI SDK, Anthropic Agent SDK, and custom orchestration where frameworks add friction
Pinecone, Weaviate, pgvector, Turbopuffer, plus BM25 and hybrid retrieval for production-grade RAG
AWS, GCP, Azure, Vercel, Cloudflare Workers. Dockerized deployments, infra-as-code, CI/CD from day one
Evaluation suites, LLM-as-judge pipelines, production observability (Langfuse, Braintrust, OpenLLMetry), prompt regression tests
Postgres, Snowflake, BigQuery, Redis. We meet your data where it lives — no forced lift-and-shift
Frequently Asked Questions
About the Service
Anything where we're writing code to build an AI-powered capability that doesn't exist in an off-the-shelf product. That includes: LLM-powered apps and copilots, document intelligence pipelines, semantic search and RAG systems, AI features embedded into your existing product, domain-tuned models, and the production infrastructure around all of them. If the other offerings (AI Employees, AI Agents, Workflow Automation) don't map to what you need, this is the bucket.
All of them. We pick the right model for the use case based on quality, latency, cost, privacy requirements, and contextual fit. For most production systems we architect for model-swappability — the business logic shouldn't be coupled to a single provider. Anthropic's Claude 4.6 is the default for many agentic workloads; GPT-5 class models for others; open-weight models (Llama, Mistral) where privacy or cost rule out hosted APIs.
Yes — this is one of the most common engagement shapes. We work in your repo, follow your code conventions, write tests, and ship features through your existing CI/CD. We're comfortable in TypeScript, Python, Go, Ruby, and most modern stacks. We don't require adopting a new platform or framework.
Every production AI system we build ships with an evaluation suite — real examples from your domain, expected behaviors, regression tests that run on every change. We instrument production with observability (Langfuse, Braintrust, or your tooling of choice), set cost and quality guardrails, and establish escalation paths for model failures. "It worked in the demo" is not a shipping criterion.
Getting Started
Fixed-price by project scope, not hourly. Smaller builds (a single LLM-powered feature, a narrow document pipeline) typically run $10,000–$25,000. Mid-size applications with custom UI, integrations, and evaluation run $25,000–$75,000. Large, production-grade platforms with domain-tuned models scale beyond that. Every scope starts with an Assessment so the price reflects real requirements, not guessing.
Single features: 3–6 weeks. Mid-size applications: 6–12 weeks. Large platforms with custom infrastructure or domain-tuned models: 12–20 weeks. We ship in weekly increments during the build phase so you see working software early, not at the end.
We design for privacy from the architecture up. For sensitive data: dedicated model endpoints or on-prem/VPC deployment, no training on your inputs, data residency controls, encryption at rest and in transit, full audit logs, and role-based access. For regulated industries we work with your compliance team on SOC 2, HIPAA, GDPR, and industry-specific requirements.
We strongly recommend it. Most successful custom AI programs start with one narrowly-scoped feature or tool, validate that it works and that people use it, and then expand. Every build is designed for modular extension — the infrastructure from the first project accelerates the second and third.
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Related offerings
AI Employees
Long-running AI workers that fill a specific role — receptionist, SDR, data-entry clerk, researcher.
AI Agents
Autonomous agents for bounded, repeatable tasks — browser automation, document processing, research.
Workflow Automation
End-to-end process automation connecting your CRM, ERP, accounting, and operational tools.