Enterprise AI

Integrate the best, validate the rest.

We build enterprise AI systems that survive contact with production. Vendor-neutral model routing. Eval harnesses in CI. Guardrails, audit logs, and cost controls wired in from day one — so the model you saw in a notebook is the model your users actually get.

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Eval-gated rollouts

No model ships without a regression suite

2.1MAgent runs per day across deployed systems

99.8%Tier-1 intent accuracy on routed conversations

< 700msMedian voice agent end-to-end latency

6 wksFrom kickoff to first agent in production

The gap between pilot and production

Why most enterprise AI programs stall — and the way we engineer around each failure mode.

What goes wrong with most AI programs

The demo works. Production doesn't.

closeToo many pilots, no production. Nobody owns hardening, governance, and the run.
closeVendor lock-in dressed up as a platform. One model, one cloud, one account manager — and no exit.
closeHallucinations and PII leakage surface in production because no one wrote guardrails for them in dev.
closeToken costs explode. There's no caching, no model routing, no budget per workflow.
closeAudit and compliance teams arrive at launch — and the project gets paused for six months.

What we build instead

Systems engineered for the third year, not the launch demo.

checkEval harnesses in CI from week one — no prompt or model change ships without a regression score.
checkVendor-neutral routing across Anthropic, OpenAI, open source, and domain fine-tunes. Switching a model is a config change, not a rewrite.
checkPII / PHI redaction, hallucination guardrails, and refusal policies wired before the first user sees the system.
checkMulti-model routing, semantic caching, and per-workflow cost budgets — token spend that scales sub-linearly with usage.
checkAudit logs, decision traces, and FedRAMP / HIPAA / SOC 2 alignment included in the build, not added at the end.

What we actually build

Four kinds of AI work make up most of our delivery. Every one of them ships with evals, guardrails, and observability — not as a follow-on phase.

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Agentic systems

Agents that actually act — with tools, memory, and human-in-the-loop where it matters.

check_circleTool use, structured outputs, and multi-step planning
check_circleStateful memory with replayable traces
check_circleHuman approval gates for irreversible actions
check_circlePer-step evals and cost ceilings

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LLM applications & copilots

RAG, copilots, and assistants grounded in your data — not in someone else's training set.

check_circleRetrieval pipelines tuned for your corpus, not the demo dataset
check_circleSource-cited answers with confidence scoring
check_circlePrompt versioning and offline regression testing
check_circleFallback to deterministic logic when confidence is low

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Predictive ML

Forecasting, churn, anomaly detection, and recommendation systems that earn their keep in the second year, not the first.

check_circleFeature stores and reproducible training pipelines
check_circleDrift detection with automated retraining triggers
check_circleA/B and shadow deployments before anything goes live
check_circleModel cards and bias audits as deliverables

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Voice & conversational AI

Sub-second voice agents for high-volume contact-center and citizen-services workloads.

check_circleEnd-to-end latency budgets, measured per turn
check_circleBarge-in, interruption, and graceful handoff to humans
check_circleDomain-tuned ASR and TTS with custom vocabularies
check_circleSee more on the voice AI page

How we ship

A four-phase rhythm built for AI work. Eval-driven, not demo-driven. Real users in front of the system before we scale it.

Scope

Use-case framing, data audit, success metrics, and an evaluation rubric written before any code. You leave with a fixed-fee scoping doc you own.

check_circle1–2 weeks
check_circleEval rubric written first
check_circleFixed-fee

Prototype with eval

A working prototype against your data, scored against the rubric. We show you the failure modes alongside the wins.

check_circle3–4 weeks
check_circleReal data
check_circleFailure modes published

Productionize

Guardrails, observability, cost controls, and audit logs. CI runs the eval suite on every change. Shadow deploy before live traffic.

check_circle4–8 weeks
check_circleEval-gated CI
check_circleShadow → live

Run + improve

Drift monitoring, prompt and model upgrades, and a quarterly model review. Not a handoff — a relationship.

check_circleDrift monitoring
check_circleQuarterly model review
check_circleOn-call rotation

Tech we use

Python·PyTorch·LangGraph·LangChain·LlamaIndex·OpenAI·Anthropic·Bedrock·Vertex AI·Azure OpenAI·vLLM·Triton·MLflow·Weights & Biases·Snowflake·Databricks·Pinecone·pgvector

Governance

Responsible AI is an engineering problem

Not a slide. Every system we ship has the same set of controls wired in from day one — because retrofitting them at audit time is how AI projects get killed.

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PII / PHI redaction & tenant isolation

Inbound and outbound. VPC, private-link, and self-hosted options. No sensitive data sent to third-party model providers without explicit policy.

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Eval harnesses in CI

Every prompt change, model upgrade, and retrieval tweak runs against your regression suite before it merges. Drift alerts and rollback policies built in.

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Hallucination guardrails

Source grounding, confidence thresholds, and refusal policies tuned per use case. The model says "I don't know" when it doesn't.

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Audit logs & decision traces

Every agent step, retrieval hit, and tool call is signed, logged, and replayable. Compliance teams get the trail they need.

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Vendor-neutral model routing

Route each task to the right model — frontier when it matters, small open-source when it doesn't. Cost-aware routing, fallback policies, per-task caps.

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Reversible by default

Every write action is preview-first and undoable. Shadow mode before go-live. FedRAMP / HIPAA / SOC 2 alignment built into the engagement, not bolted on at audit.

Pre-built AI products

Already know what you want?

Eight productized accelerators across recruiting, commerce, legal, immigration, marketing, and security — each with a fixed-fee pilot plan.

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Outcomes, not slideware

Production case studies.

Six engagements across logistics, legal, hospitality, retail, entertainment, and AI startups — with the numbers that mattered.

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Client voices

The work speaks; our customers say it louder

We are measured by outcomes — containment, lift, freshness, and spend — not by how many slides we ship.

Mudish rebuilt our intake and case-triage workflow in under a quarter. We're signing more qualified cases, our demand letters draft themselves, and every paralegal hour now goes to work that actually moves the needle on settlement.

Founder

Founder · Personal injury law firm

On every first call

Questions enterprise buyers ask before they ever sign

The answers procurement, security, and engineering leaders want before a follow-up meeting gets scheduled.

We architect around an abstraction layer that routes between Anthropic, OpenAI, open-source models on your own infra, and domain-specific fine-tunes. Switching a model is a config change, not a rewrite.

Yes — AWS, Azure, GCP, and Oracle Cloud, with private endpoints, BYOK, and optional air-gapped delivery for regulated workloads.

Mostly inside what you already own. Replacing a working stack is rarely the highest-ROI move; we tell you when it is and when it isn't.

Because we start from pre-built accelerators, most engagements have a working pilot within 4–6 weeks and a production rollout within a quarter.

Fixed-fee discovery, then a blended team retainer for build. We also do outcome-based pricing tied to metrics like cost-per-hire, containment rate, or conversion lift.

Extensively. Federal, healthcare, financial services, and legal — we deliver against FedRAMP Moderate, SOC 2 Type II, HIPAA, and Section 508 baselines.

Most AI projects don't fail because the model was wrong.

They fail because the system around the model wasn't built. Tell us what you're trying to ship — a senior engineer replies within one business day.

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