AI and LLM engineering that holds up in production.
Engineers who take AI capabilities beyond prototypes and into production systems that customers and teams actually rely on. We build retrieval-augmented generation, LLM integrations, and AI agents that work against your real data and real systems, with the permissions, evaluation, and audit logs that production requires.
AI systems wired into the way your business runs
Built for production, not for the demo. The model is one component; most of the work is the system around it.
RAG and vector search
Retrieval-augmented generation over your documents and records, with chunking, embeddings, and ranking tuned to your content. Answers stay grounded in sources you control, and access rules decide what each user can retrieve.
Natural-language access to enterprise data
Let non-technical teams ask questions of scattered business data in plain language. We have built text-to-query engines, BI dashboards generated from the question itself, and document analysis that pulls insight out of PDFs, reports, and contracts.
AI agents and tool calling
Agents that act on real systems, not toy APIs. We connect them to your tools through the Model Context Protocol, give them scoped permissions, and log every action. Our support assistants resolve routine questions, qualify leads mid-conversation, and route buying intent to sales.
Agentic engineering tooling
Coding agents for generation, refactoring, and review, built for engineering teams that need to trust the output. LSP-backed code intelligence across TypeScript, Python, Rust, Go, C#, and C/C++, OS-level sandboxing, parallel tool execution, and full audit logs so review is possible.
Evaluation and fine-tuning
Evaluation sets built from your own cases, so model and prompt changes are measured before they ship. Fine-tuning when a general model is not enough for the domain, and only when evaluation shows it earns its cost.
AI in regulated environments
Enterprise SSO, role-based access control, and audit trails built in from the start. We have shipped AI-driven patient intake inside a clinical platform and AI data tools behind enterprise authentication.
What changes between the demo and the system people rely on
AI prototypes are easy. Production systems aren’t. A demo answers the questions it was shown. A production system meets messy data, unclear permissions, legacy integrations, and users who will find every edge case in the first week.
We treat that gap as the engineering work. Audit logs, observability, and rollback paths are the floor, not a feature.
- Grounded answers. Retrieval over sources you control, with the source visible to the user where it matters.
- Permissions that reach the model. SSO and role-based access decide what an agent or query can see, not just what the interface shows.
- Evaluation before release. Changes to prompts, models, or retrieval are tested against your own cases.
- Every action logged. Agent tool calls and data access are written to an audit log you can review.
- Monitoring and a way back. Usage, cost, and failure monitoring in production, plus a rollback path for every change.
The AI work rarely stands alone. It depends on data engineering that keeps sources clean and current, and on full-stack engineering that puts it in front of users.
Tools we work with
We pick the stack that fits your data, your constraints, and the systems already in place.
Retrieval and search
Vector stores and retrieval for grounded answers.
Agents and tools
Agents connected to real systems with clear boundaries.
Quality and control
How we know it works, and who can use it.
How you can engage
The same engineers, three ways to work with them. Pick the model that matches how defined the problem is and how much ownership you want to hand over.
Forward Deployed Engineering
An FDE works with your team to find where AI will actually help, shapes the approach, and leads a pod that builds, integrates, and improves it. Best when the use case is still taking shape.
How Forward Deployed Engineering works →Dedicated Engineering Pod
A senior-led pod owns a defined AI product or feature, from architecture and retrieval design through deployment, and reports against milestones.
Build a dedicated engineering pod →Embedded Engineers
Senior AI and LLM engineers join your standups, sprint, and tools to add depth your team does not have yet. You direct the work day to day.
Add senior engineers to your team →AI systems we have shipped
Natural-language data workspace
RAG PipelineAn AI-powered workspace letting non-technical teams query enterprise data in plain language, with instant BI dashboards and document analysis.
enforced
AI website support & lead qualification
Agent RoutingA grounded AI assistant that resolves routine support questions, captures qualified leads mid-conversation, and routes buying-intent visitors to sales.
coverage
Clinical practice operating system
Distributed LocksAn end-to-end platform taking a patient from AI-driven intake through booking, chairside charting, and billing on one record.
by design
Common questions
Can you take an existing AI prototype into production?
Yes, and it is one of the most common starting points. We keep what works, then add what production needs: integration with your real systems and data, permissions, evaluation against your own cases, monitoring, and audit logs. Your FDE starts by testing the prototype against real data and real workflows before anything is scaled.
Which vector databases and retrieval approaches do you use?
We choose per project. For retrieval we work with pgvector when the data already lives in PostgreSQL, and with dedicated stores such as Pinecone or Weaviate when the workload calls for one. The choice follows your data, your access rules, and where that data is allowed to go.
How do you keep AI agents safe when they act on real systems?
Agents get scoped permissions, not open access. Tool calls run through defined interfaces such as MCP, execution can be sandboxed at the operating-system level, and every action is written to an audit log so it can be reviewed. Where a decision carries risk, a person stays in the loop.
Do you build AI for regulated environments?
Yes. We have shipped AI workspaces with enterprise SSO and role-based access control, and platforms in healthcare and legal settings where the audit trail matters as much as the feature. Access rules apply to what the model can retrieve, not just to the interface.
Tell us what you’re trying to build.
A short conversation is enough to understand the problem, the systems involved, and the team required to move it forward.