Enterprise AI

Build AI systems for production use.

We build and operate enterprise AI systems with vendor-neutral model routing, evaluation in CI, and clear controls for data, cost, and access. We test changes against your use cases before releasing them to users.

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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

Common obstacles to deploying enterprise AI and how we address them during development.

Common delivery challenges

Moving a prototype into production

closePilots stall when ownership of hardening, governance, and ongoing operations is unclear.
closeDependence on one model or cloud provider makes migration and vendor changes difficult.
closeHallucinations and PII leakage can reach users when testing and data controls are incomplete.
closeToken costs rise without caching, model routing, or budgets for individual workflows.
closeLate audit and compliance reviews can delay a planned launch.

Our engineering approach

Systems designed for ongoing operation

checkEvaluation in CI from week one, with a regression score required for prompt and model changes.
checkVendor-neutral routing across Anthropic, OpenAI, open source, and domain fine-tunes, with a shared interface for configuring and testing model changes.
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 to manage token spend as usage grows.
checkAudit logs, decision traces, and FedRAMP / HIPAA / SOC 2 alignment addressed during development.

What we build

Our AI delivery covers four areas, with evaluation, guardrails, and monitoring included in development.

smart_toy

Agentic systems

Agents that use tools and memory to complete workflows, with human review at defined decision points.

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

psychology

LLM applications & copilots

RAG applications, copilots, and assistants that retrieve and cite your business data.

check_circleRetrieval pipelines tuned for your corpus
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 prediction, anomaly detection, and recommendation systems with ongoing monitoring and maintenance.

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

We define evaluation criteria, test with your data and users, then prepare the system for production and ongoing operation.

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 to 2 weeks
check_circleEval rubric written first
check_circleFixed-fee

Prototype with eval

A working prototype against your data, scored against the rubric. We document successful results and failure modes.

check_circle3 to 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 to 8 weeks
check_circleEval-gated CI
check_circleShadow → live

Run + improve

Ongoing drift monitoring, prompt and model upgrades, and a quarterly model review.

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

AI governance through engineering controls

We define data, evaluation, and audit controls during design and carry them through development, testing, and ongoing operation.

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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, with fallback responses when evidence or confidence is insufficient.

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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

Select models by task requirements, using frontier and smaller open-source models with cost-aware routing, fallback policies, and per-task caps.

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Controlled changes

Preview and approval steps for write actions, with rollback where supported. Shadow mode before go-live and FedRAMP / HIPAA / SOC 2 alignment during delivery.

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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Project outcomes

Production case studies.

Six engagements across logistics, legal, hospitality, retail, entertainment, and AI startups, with reported results.

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Client voices

What our customers say

Clients describe the changes to their workflows, costs, and service delivery.

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 about enterprise AI delivery

Details on deployment, integration, timelines, and commercial terms.

We use a shared routing layer for Anthropic, OpenAI, open-source models on your infrastructure, and domain-specific fine-tunes. This gives your team a consistent interface for configuring and testing model changes.

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

Most of our work builds on existing systems. We assess the current stack and recommend replacement only when its limitations justify the cost and disruption.

Because we start from pre-built accelerators, most engagements have a working pilot within 4 to 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.

We work in federal, healthcare, financial services, and legal settings, with delivery aligned to FedRAMP Moderate, SOC 2 Type II, HIPAA, and Section 508 baselines.

Discuss your next AI project.

Tell us about your use case, data, and deployment requirements. A senior engineer replies within one business day.

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