Applied AI Studio · Los Angeles, CA

We build the intelligence layer
your business runs on.

Donald Hans integrates production-grade AI into your products and operations — and trains custom models when off-the-shelf isn’t enough. Strategy, engineering, and a measurable edge, from one team.

120+
AI systems shipped
40+
Custom models trained
Avg. workflow speed-up
99.9%
Production uptime

Building on the frontier — and on your stack

OpenAIAnthropicLlama 3MistralHugging Face NVIDIALangGraphPineconeSnowflakeAWS Bedrock OpenAIAnthropicLlama 3MistralHugging Face NVIDIALangGraphPineconeSnowflakeAWS Bedrock

Built by engineers from

Georgia Tech Google Amazon

Our software and AI engineers are Georgia Tech alumni with experience building at Google and Amazon — frontier craft, applied to your business.

The flagship

Two ways we put AI to work

Most teams need both — a fast path to value with existing models, and a moat built from models that are truly theirs. We do the full arc.

Capabilities

A full-stack AI team, on demand

From the first data audit to a model serving traffic in production — the whole pipeline under one roof.

01 · Agents

Autonomous agents & orchestration

Multi-step agents that plan, call your tools and APIs, and recover from failure — wired with tracing, evals and budget limits so you can actually trust them in production.

LangGraphTool useMCP TracingEvals

Retrieval & RAG

Hybrid search over your knowledge, grounded answers with citations.

Evals & quality

Offline + online eval suites so quality never silently regresses.

Fine-tuning & distillation

Smaller, faster, cheaper models tuned to your domain and SLAs.

Governance & safety

Guardrails, PII handling, audit logs and policy controls baked in.

Enterprise

AI agents, built for the enterprise

Autonomous agents that do real work across your enterprise systems — with the security, governance and reliability a large organization actually requires. Not a chatbot: a digital workforce you can put into production and trust.

  • Governed — SSO/SAML, role-based access, full audit trails & policy controls
  • Connected — acts across your CRM, ERP, ITSM, data warehouse & comms tools
  • Controlled — human-in-the-loop approvals & guardrails on every action
  • Private — deploy in your VPC or on-prem; your data never leaves your walls
Enterprise agent live
agent · acme-itops · run #2841
# incident: payments API p95 latency ↑
correlated Datadog + Snowflake logs
opened ServiceNow INC-4471
drafted rollback plan & impact
awaiting SRE approval (RBAC)

Connected systems

SalesforceServiceNowSAP SlackSnowflakeWorkday

Governance & security

SOC 2SSO / SAMLRBACAudit logVPC / on-prem

IT & DevOps

Triage alerts, run runbooks and resolve tickets across your stack.

Customer Operations

Resolve and escalate cases end-to-end with full account context.

Finance & Procurement

Invoice matching, approvals and spend analysis on autopilot.

Risk & Compliance

Monitor, flag and document with a complete, exportable audit trail.

Production-grade by default

Not a demo. A system you can ship.

Pilots are easy; production is the hard part. Every engagement ships with the plumbing real software needs — observability, evaluation, cost controls and a clean handoff your engineers can own.

Instrumented end to end

Traces, token + latency metrics and eval scores on every request.

Cost & latency under control

Routing, caching and distillation keep spend and p95 predictable.

Yours to own

Documented code, infra-as-code and a walkthrough with your team.

dh-agent · train.py
# fine-tune a domain model on your data
from donaldhans import Studio
studio = Studio(project="acme-support")
model = studio.finetune(
base="llama-3.1-8b",
data="tickets.jsonl", epochs=3,
eval="golden-set.jsonl",
)
# → deploying private endpoint…
✔ eval accuracy 94.6% (+18.2)
✔ p95 latency 310ms (-71%)
✔ cost / 1k req $0.42 (-83%)
endpoint ready
faster internal workflows
83%
lower inference cost
6 wks
median time to production
40+
models in production
Solutions

Where the intelligence layer pays off

We bring the same AI core to the disciplines we’ve always served — now supercharged.

How we work

From idea to production in four moves

Step 01

Discover

We map the workflow, data and the highest-leverage place AI changes the math.

Step 02

Prototype

A working slice in weeks, measured against a golden set — not a slide deck.

Step 03

Productionize

Hardening, evals, observability and the model work to hit your SLAs.

Step 04

Scale & own

We hand off clean, documented systems — and stay on as far as you want.

“Donald Hans took us from a clever prototype to a model serving our whole support org in six weeks. Cost dropped 80% and quality went up.”

VP Engineering · Series-B SaaS
Let’s build

Put AI at the center of what you do.

Tell us the workflow you want to transform. We’ll come back with a concrete plan, a prototype scope and a timeline — usually within a week.