Do you build custom agents, or wrap a generic chatbot template?

Every agent is custom-designed for your workflow, with goals, tools, memory and evals scoped to your systems rather than a one-size-fits-all template.

How do you know an agent is actually working in production?

Offline and online evals, golden datasets, full tracing, regression detection, drift alerts and cost dashboards.

How do you handle PHI or sensitive data in AI workflows?

BAA-covered model providers where required, PHI/PII minimization at every step, and audit trails and role-based access built in, not bolted on.

Services

ML/AI Engineering

From R&D to production-grade AI systems.

We take AI from a research question to a production system: model selection and R&D spikes, RAG and retrieval pipelines, fine-tuning where it earns its cost, and agentic systems with the evals, guardrails and observability that make them trustworthy in production.

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What's included

Everything it takes to ship this, end to end.

R&D & model selection

Spikes that test feasibility against your real data before committing to an architecture or vendor.

RAG & retrieval

Vector, hybrid and geospatial search pipelines grounded in your own content, with citations, not hallucinated answers.

Agentic systems

Multi-agent architectures with tool-use, memory and routing, built for the workflow, not a generic template.

Fine-tuning & prompt engineering

Task-tuned models and structured-output prompting where it measurably beats a bigger general model.

Computer vision & speech

Vision models for document/image understanding and speech-to-text pipelines for voice-driven flows.

Evals & observability

Golden datasets, offline/online evals, drift alerts and cost dashboards so quality is measured, not assumed.

How we run it

Four stages, one accountable team.

R&D spike

A short, focused spike to test feasibility against your real data before committing to a build.

Design the system

Architecture, tool contracts, guardrails and success metrics defined before implementation starts.

Build & evaluate

Iterative development against golden datasets and human-in-the-loop scoring, not vibes.

Deploy & operate

Production deployment with tracing, drift alerts and managed ops to keep quality from decaying.

Agent patterns

Patterns we ship, again and again.

Voice & Intake Agents

Always-on multilingual voice agents that book, triage, verify insurance, collect intake forms, and warm-transfer to humans on edge cases.

Sub-700ms latency
EHR + scheduling integrations
PHI redaction & call recording controls

Clinical Scribe & Documentation Agents

Ambient agents that listen to encounters, draft SOAP notes, suggest ICD-10/CPT codes, and post structured data into your EHR.

Specialty-tuned templates
Clinician-in-the-loop review
EHR write-back via FHIR / APIs

Revenue Cycle Agents

Autonomous agents for eligibility, prior auth, claims submission, denials and AR follow-up, closing the loop without offshore queues.

Payer portal automation
Denials root-cause loops
Audit-grade activity logs

Knowledge & Research Agents

Retrieval-augmented agents grounded in your SOPs, contracts, clinical guidelines and literature, with answers backed by citations.

Hybrid retrieval (BM25 + vector)
Source-cited responses
Per-document access control

Patient Engagement Agents

Proactive outreach via SMS, WhatsApp, email and voice for adherence, care-plan check-ins, no-show recovery and outcomes-driven nudges.

Conversational over scripted
Escalation to care team
Tied to outcome KPIs, not opens

Internal Ops Copilots

Agents wired into Slack, Jira, Notion, Linear and your data warehouse, so they don't just chat, they execute on your tools.

Tool-use with permissions
Approvals + dry-runs
Per-team policy guardrails

Tech stack

What we build it with.

Models: OpenAI, Claude, Gemini and Llama, selected per task instead of defaulted to a single vendor.
OpenAI GPTClaudeGeminiLlama
Frameworks: LangGraph, LlamaIndex, CrewAI and Mastra for the orchestration, memory and tool-use layer around the model.
LangGraphLlamaIndexCrewAIMastra
Retrieval: Pinecone, pgvector, Weaviate and MongoDB Atlas Search, matched to your data's scale and query pattern.
PineconepgvectorWeaviateMongoDB Atlas Search
Infra: FastAPI and Python services on AWS Lambda and Docker, built to run evals and inference at production load.
FastAPIPythonAWS LambdaDocker

Also in the toolkit: OpenAI, Anthropic Claude, MCP, Whisper.

Impact

Agents and models that get evaluated like production software, so quality regressions get caught before customers notice, not after.

FAQ

Questions we hear the most.

Can't find what you're looking for? Reach out and we'll answer directly.

Tell us the project. We'll come back with a plan.

A 30-minute working session to scope ml/ai engineering against your real systems and timeline.

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