What counts as a production-ready AI system?

Monitoring for latency, cost, drift, and output quality. A rollback path. An on-call rotation or handover. Without those, it is a demo.

Do you build on top of our existing data warehouse?

Yes. Most LLM and forecasting pipelines pull from the warehouse you already have. We avoid adding stores unless the workload truly needs one.

What if the business case is unclear?

We run a short scope phase first. If the ROI does not hold up, we say so and propose a smaller pilot. We would rather not start than ship something that gets shelved.

How do you handle hallucinations?

Evaluation harnesses, retrieval quality work, prompt regression tests, and a guardrail layer. Where the domain needs it, we add human-in-the-loop review.

Can you run the system for us after launch?

Yes. See /managed-services/mlops/ for the day-two shape.

AI & Automation. AI that runs in production.

Applied AI with clear owners and measurable wins. We ship systems that move a number. LLM pipelines, document intelligence, forecasting, anomaly detection. Each engagement clears a production-readiness bar before we call it done.

Talk to us All projects

What we do

What we build.

LLM pipelines and RAG

Retrieval-augmented generation over your documents, databases, or tickets. Chunking, embeddings, evals, guardrails. Shipped with a monitoring harness.
Document intelligence

Extraction, classification, and routing for invoices, contracts, claims, and compliance artifacts. Accuracy gates agreed before go-live.
Forecasting

Demand, churn, or financial forecasting that beats the baseline by a margin worth shipping. Backtested before production.
Anomaly detection

Signals in fraud, fleet, operations, or telemetry. Tuned for the false-positive rate you can staff for.
Evaluation and guardrails

The layer most teams skip. Prompt regression tests, hallucination checks, PII filters, and output validation before anything ships.
Model governance

Versioning, audit trails, dataset lineage, and access control. Enough for SOC 2 and the kinds of reviews your customers will run on you.

Stack

Tools we use most.

OpenAI
Anthropic
Azure OpenAI
Databricks
Snowflake Cortex
LangChain
LlamaIndex
Pinecone
Weaviate
pgvector
Pydantic
MLflow
Vertex AI
SageMaker
Evidently
Arize

How it works

Engagement shape.

Typical length: 6 to 12 weeks
Team shape: 3 to 5 people
Output: Running system + runbook
Pricing: Fixed scope

FAQ

Questions we get.

What counts as a production-ready AI system?: Monitoring for latency, cost, drift, and output quality. A rollback path. An on-call rotation or handover. Without those, it is a demo.
Do you build on top of our existing data warehouse?: Yes. Most LLM and forecasting pipelines pull from the warehouse you already have. We avoid adding stores unless the workload truly needs one.
What if the business case is unclear?: We run a short scope phase first. If the ROI does not hold up, we say so and propose a smaller pilot. We would rather not start than ship something that gets shelved.
How do you handle hallucinations?: Evaluation harnesses, retrieval quality work, prompt regression tests, and a guardrail layer. Where the domain needs it, we add human-in-the-loop review.
Can you run the system for us after launch?: Yes. See /managed-services/mlops/ for the day-two shape.

AI & Automation. AI that runs in production.

What we build.

LLM pipelines and RAG

Document intelligence

Forecasting

Anomaly detection

Evaluation and guardrails

Model governance

Tools we use most.

Engagement shape.

Questions we get.

See also.

Tell us what you want to move.