Off-the-shelf AI tools solve generic problems. Your organization has specific data, specific workflows, and specific compliance constraints that generic tools weren't designed for. We build custom AI systems — from LLM integrations and RAG pipelines to agentic workflows and fine-tuned models — engineered precisely for your environment.
We don't drop a generic API wrapper into your stack and call it AI. We architect systems built around your data, your workflows, and your compliance requirements — delivering AI that actually performs in production.
We design and implement LLM integrations that connect frontier models — GPT-4o, Claude, Llama, Gemini, and others — directly to your systems, data, and workflows. That includes API architecture, prompt engineering, context management, rate limiting, cost controls, and the evaluation pipelines that tell you whether the model is actually performing as intended.
Retrieval-Augmented Generation lets an LLM answer questions grounded in your organization's specific documents, databases, and knowledge bases — without hallucinating or requiring expensive fine-tuning. We design and build production-grade RAG systems: document ingestion pipelines, embedding strategies, vector store selection, hybrid search, and retrieval optimization that keeps answers accurate and latency low.
When a general-purpose model needs to behave differently — matching your brand voice, mastering domain-specific terminology, or reliably producing structured output in a particular format — fine-tuning is the right tool. We manage the full pipeline: dataset curation, training runs, evaluation benchmarking, and deployment, using the most cost-effective approach for your performance requirements.
Agentic AI systems can plan, execute multi-step tasks, call external tools, and loop until they reach a goal — eliminating entire categories of manual work. We design agentic architectures for your highest-leverage automation opportunities: document processing, research pipelines, customer service escalation, internal knowledge retrieval, and complex multi-system workflows that previously required human coordination.
Regulated organizations can't simply pass patient records, financial data, or sensitive PII to a third-party LLM API and call it done. We architect AI systems that respect your compliance obligations — using on-premise or private cloud model deployments where required, implementing PII redaction before data reaches any model, and ensuring your AI pipeline produces evidence for audit trails mandated by HIPAA, GDPR, or the EU AI Act.
AI capabilities are most valuable when they're embedded seamlessly in the products and tools your teams already use. We build the API layers, webhooks, and integration connectors that bring AI functionality into your existing CRM, EHR, document management system, or custom application — without requiring your teams to change how they work.
We're model-agnostic and framework-agnostic. The right tool depends on your requirements — performance, cost, privacy, latency, and the specific capability you need. We evaluate the options objectively and recommend based on your constraints, not vendor preferences.
For regulated organizations, on-premise or private cloud deployment is often required. We have production experience deploying open-source models in air-gapped and private cloud environments that satisfy HIPAA and GDPR requirements.
Every AI engagement starts with a deep understanding of the problem, the data, and the constraints. We work with your technical and business teams to define the use case precisely, evaluate available data and its quality, assess compliance obligations, and design an architecture that fits your environment — before writing a line of code.
Before committing to a full build, we deliver a functional proof of concept — enough to validate the approach, surface unexpected data or performance issues, and give your team something concrete to evaluate. We instrument evaluation pipelines from day one so you have real performance metrics, not subjective impressions of whether the AI is performing as intended.
With the architecture validated, we build the production system — hardening the pipeline, integrating into your existing stack, implementing safety guardrails, adding observability, and ensuring the system performs reliably at the scale and latency your use case demands. Everything is documented for your engineering team to maintain and extend.
AI systems degrade — data drifts, user behavior changes, model providers update their APIs. We deploy with monitoring in place and provide ongoing support to keep performance high: tracking real-world outputs against evaluation benchmarks, catching regressions before users do, and iterating on prompts, retrieval strategies, and model selection as better options emerge.
The same underlying technologies — RAG, agents, fine-tuning — solve fundamentally different problems depending on the industry and workflow. Here are some examples of how we can apply AI across sectors.
RAG pipelines that let clinical teams query patient records, clinical guidelines, and internal protocols in natural language — with full HIPAA compliance and audit logging.
AI agents that monitor regulatory filings, extract relevant rule changes, and generate compliance impact summaries — reducing hours of manual review to minutes.
Custom LLM systems that extract key terms, flag non-standard clauses, and surface relevant precedents from large document repositories — trained on your firm's specific standards.
RAG-powered knowledge bases that make years of internal documents, policies, and institutional knowledge queryable — so staff spend less time searching and more time executing.
Custom AI features embedded directly into your product — intelligent search, content generation, user onboarding automation, and natural language interfaces built around your data model.
Agentic systems that handle multi-step operational tasks — intake processing, report generation, data enrichment, and cross-system coordination — without human intervention in the loop.
Tell us about your use case, your data, and your constraints. We'll assess feasibility, recommend an architecture, and scope a build that delivers measurable results.