Custom AI systems.

Bespoke AI for problems APIs can't solve.

Off-the-shelf APIs are great until they aren't. The model hallucinates on your domain. Latency is too high. The data can't leave your network. The cost doesn't scale. We design custom AI systems for the moments when calling OpenAI is not the right answer.

• Retrieval-augmented generation (RAG) pipelines — chunk, embed, retrieve, rerank, cite. Tuned to your data and your latency budget.
• Fine-tuning — supervised, instruction, and preference tuning on OpenAI, Anthropic, and open-source models. With the eval harness that makes it durable.
• Multi-agent orchestration — coordinated agents with custom tools, hand-offs, and shared state. Built on LangGraph, CrewAI, or bespoke frameworks.
• On-prem and VPC deployment — open-source models (Llama, Mistral, Qwen) running inside your network, with no data leaving the perimeter.
• Eval and observability platforms — eval harnesses, trace dashboards, quality monitoring, and regression detection for AI systems in production.
• Hybrid architectures — public APIs for general work, private models for sensitive workflows, routing logic that picks the right model for the job.

Teams that have outgrown off-the-shelf.

Engineering teams that need grounding on private data the public models don't have. Companies in regulated industries (healthcare, finance, legal, defense) where data must stay inside the perimeter. AI products that have hit a quality ceiling and need fine-tuning or retrieval to break through. Multi-step workflows that need coordinated agents, not a single prompt.

If you've already tried the off-the-shelf route and it's not working, that's usually the right time to talk.

From architecture to production in 4–8 weeks.

1. Audit — 30-minute call. We review the system, the data, the constraints, and the failure modes. We confirm a custom system is the right tool (and that you don't just need better prompts).
2. Design — Fixed-price quote in 48 hours. Architecture diagram, data flow, model choice, deployment plan, eval plan, and cost projection.
3. Ship — Working system in week one. We build against your real data and your real traffic patterns, with a clear path to production.
4. Operate — Optional month-to-month care: we monitor quality, retrain as needed, and improve the system as your data and requirements evolve.

Where the system runs.

• Fully managed (OpenAI, Anthropic, Google) — fastest to ship, lowest upfront cost, best for non-sensitive workloads.
• VPC on AWS / GCP / Azure — public models reached through your private network, with private data stores and audit logging.
• On-premise (Llama, Mistral, Qwen) — fully self-hosted, no data leaves the perimeter, suitable for regulated and defense workloads.
• Hybrid — public APIs for general work, private models for sensitive workflows, with routing logic that picks the right tool for the job.

Every decision is explainable.

Custom AI systems are only useful if you can trust them — and trust requires evidence. We design for review, not magic:

• Trace logging — every model call, retrieval, and tool invocation is logged with input, output, latency, and trace ID.
• Eval harnesses — automated quality checks on every change, with regression detection against the previous version.
• Quality dashboards — visual monitoring of accuracy, latency, cost, and edge-case rates over time.
• Explainable outputs — for regulated workflows, the system returns not just an answer but the evidence it used and the reasoning it followed.

Frequently asked questions.

When do I need a custom AI system instead of an off-the-shelf API?

When off-the-shelf APIs can't meet your accuracy, latency, privacy, or cost requirements. Typical reasons: you need grounding on private data the models don't know, you need deterministic behavior for a regulated workflow, you need to run inside a private network for compliance, or you need a multi-step agent with custom tool use.

Do you fine-tune models?

Yes. We do supervised fine-tuning, instruction tuning, and preference tuning on OpenAI, Anthropic, and open-source models. We also build the eval harness and data pipeline that make fine-tuning durable, not a one-shot exercise.

Can you deploy inside our VPC or on-premise?

Yes. We deploy on AWS, GCP, Azure, or on-prem Kubernetes. For highly regulated workloads, we run open-source models (Llama, Mistral, Qwen) inside the customer's network with no data leaving the perimeter.

How do you make custom AI systems auditable?

Every model call, retrieval, and tool invocation is logged with input, output, latency, and trace ID. We build dashboards so your team can inspect, replay, and explain every decision the system makes — to other engineers, to users, or to auditors.

What about cost? Isn't custom more expensive than an API?

Upfront, yes. At scale, often no — especially for high-volume or latency-sensitive workloads where inference cost and per-call latency dominate. We model the cost trade-off in the design phase so you know what you're getting into.

More ways we put AI to work.

• AI automation for operations — agents that take over inbox triage, ticket routing, CRM updates, and approvals.
• AI product development for startups — AI-native SaaS, internal tools, and prototypes, shipped end-to-end.