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AI Practice · Enterprise-grade AI

Purpose-built AI
for the enterprise.

Not AI demos. Not pilot programs that go nowhere. We design and deploy production AI systems — from LLM integration to custom agents to AI infrastructure — built to run at scale.

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AI deployment pipeline — production
Model deployed
gpt-4o · 847ms p50 latency
RAG pipeline active
2.4M documents indexed · 98.2% recall
Agent loop running
23 tools · 99.7% task completion
Guardrails enabled
PII redaction · output validation
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PRODUCTION AI, ENGINEERED

From scattered data to intelligence in production

Six stages turn raw sources into a monitored, governed AI system that ships — scroll to watch it assemble.

PRODUCTION AI PIPELINE
Data Sources
APIs · DBs · Docs
Preprocessing
Clean · Chunk · Embed
Model Layer
Fine-tune · Prompt
Inference
Route · Cache · Scale
Prod API
Auth · Rate limit · Logs
Monitoring
Drift · Cost · Quality
DATA IN →
→ INSIGHT OUT
RAG Architecture
LLM Integration
Fine-Tuning
Agents
Infrastructure
AI Governance
Connect every source of truth
Normalize, chunk & embed
Adapt models to your domain
Route & scale every request
Serve securely under load
Watch drift, cost & quality
0req / minsustained throughput in production
Our AI approach

AI that ships to production, not to PowerPoint.

Most organizations have tried AI. Few have deployed it at production quality. We bridge that gap — not by selling you a platform, but by building the engineering scaffolding that makes AI reliable, observable, and safe.

Our practitioners have backgrounds in production ML at Google, AWS, and other organizations where AI is infrastructure — not a feature flag. We bring that discipline to your stack.

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30+
AI deployments
99.7%
Task completion avg
6
AI model families
<2wk
First deploy time
AI capabilities

The full AI engineering stack.

From LLM integration to production monitoring, we cover every layer of AI delivery.

LLM Integration

Production integration of GPT-4, Claude, Gemini, Mistral, and open-source models into your existing systems and workflows.

OpenAI · Anthropic · Gemini · Ollama
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RAG Architecture

Retrieval-augmented generation pipelines that ground AI responses in your data — with embeddings, vector search, and semantic chunking built for accuracy.

Pinecone · Weaviate · pgvector · LlamaIndex
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AI Agents

Multi-step autonomous agents that call tools, use APIs, query databases, and complete complex workflows with human-in-the-loop controls.

LangChain · CrewAI · AutoGen · custom
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Fine-tuning & Training

Custom model training and fine-tuning on your proprietary data — PEFT, LoRA, RLHF — for domain-specific tasks that general models can't cover.

LoRA · PEFT · DPO · vLLM
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AI Infrastructure

GPU clusters, inference serving, model registries, feature stores, and MLOps pipelines — the production-grade scaffolding AI requires.

MLflow · Kubeflow · Ray · Triton
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AI Governance

Responsible AI frameworks, output validation, PII redaction, bias detection, audit logging, and regulatory compliance for enterprise and federal deployments.

Guardrails · Evals · NIST AI RMF
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Deployment process

From first conversation to production AI.

01

Use-case
Definition

We identify the highest-value AI use cases based on your data, workflows, and business objectives — not what's trending.

02

Architecture
Design

We design the full AI system — model selection, data pipeline, serving infrastructure, observability, and fallback logic.

03

Build &
Deploy

We build, evaluate, and deploy to production — with eval harnesses, CI/CD pipelines, and human review gates where required.

04

Monitor &
Improve

Ongoing monitoring for drift, regression, and quality degradation — with retraining pipelines and model version management.

FAQs

AI questions, answered.

Have a different question? Talk to an AI practitioner directly.

Talk to an engineer

API access is the easy part. We add the engineering layer: evaluation harnesses, RAG pipelines, guardrails, observability, cost optimization, and the production infrastructure that makes AI reliable at scale — not just in a demo.

We design for data minimization and privacy from the start. Depending on your requirements, we implement PII redaction, on-premise or VPC-contained inference, zero-retention API configurations, and audit logging. Federal and healthcare clients are a specialty.

We are model-agnostic. We work with OpenAI, Anthropic, Google Gemini, Mistral, Llama, and fine-tuned open-source variants. Framework-wise: LangChain, LlamaIndex, Haystack, CrewAI, and custom implementations where frameworks add unnecessary complexity.

A focused LLM integration with RAG can go from kickoff to production in 4–8 weeks. More complex agent systems with custom fine-tuning typically run 8–16 weeks. We move fast by being opinionated about what to build and what to skip.

Yes. We offer retainer-based model monitoring, drift detection, evaluation refreshes, and incremental retraining. AI systems degrade without maintenance — we keep them sharp.

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Senior AI practitioner replies within 1 business day
No SDR, no discovery-call gauntlet
First AI deployment in under two weeks
Direct contact
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