A team member wired up an OpenAI call inside a Streamlit demo, the exec team clapped, and twelve months on it is still a Streamlit demo. There's no auth, no observability, no eval harness, no cost ceiling — and no path to put it in front of a customer.

The gap between a notebook and a production LLM endpoint is the same gap as between a SQL query and a data warehouse: real engineering, with prompt versioning, schema-enforced outputs, retries, rate limits, and telemetry.

We build LLM features as proper microservices — observable, cost-controlled, and slotted into your existing stack — so the next person who asks 'is the AI thing actually live?' gets a real answer.

Without grounding, LLMs will fabricate a policy clause, a product spec, or a price. Legal sees one screenshot of a hallucination and the project is shelved indefinitely.

Retrieval-Augmented Generation is the discipline that makes LLMs shippable to customers: vector and hybrid retrieval against your own source-of-truth content, citation enforcement, and evaluation against a golden set so you catch regressions before they go live.

We build RAG pipelines that cite the source document on every answer, with retrieval evals that score recall and answer-faithfulness on every change — so the team trusts what it ships, and the legal team signs off.

A new lead lands; someone copies it into the CRM, pastes it into Slack, raises a ticket in Linear, updates a Google Sheet, and emails the account manager. Multiply by every process and you have a full-time job nobody owns, with an error rate nobody measures.

Most of this work is deterministic plumbing dressed up as 'judgment'. Zapier and Make handle the simple, fan-out cases; durable workflow engines like Temporal and Inngest handle the long-running, retry-heavy, audit-critical ones.

We map the workflows worth automating, pick the right tier of tool for each, and ship them with logging, retries, and a human-in-the-loop step where it actually matters — so the work happens reliably and your team gets the hours back.

The multi-step agent looked extraordinary on the conference stage. In production it calls the wrong tool, loops on a malformed response, burns through tokens, or returns JSON that breaks the next system in the chain.

Production agents need structured tool calls, schema-validated outputs, retry and timeout budgets, sandboxed execution, evaluation harnesses, and observability into every step. Without those, an agent is a non-deterministic bug generator pointed at your customers.

We build agents on the patterns that survive contact with real traffic — explicit tool contracts, eval-gated deploys, step-level tracing, and guardrails that fail closed — so the agent does its job and your on-call engineer sleeps.

Support tickets, sales call transcripts, reviews, survey free-text, contract clauses — gigabytes of signal nobody can act on, because reading it manually doesn't scale and the old keyword classifiers stopped working in 2019.

Modern transformer-based NLP — entity extraction, intent and sentiment classification, semantic search, clustering — turns that pile into rows in a table the business can query. Routed tickets, tagged calls, themed reviews, searchable contracts.

We pick the right model for the task (often smaller, cheaper, and fine-tuned beats a frontier LLM by a wide margin), wire it into the systems that already own the workflow, and validate it against a labelled set so accuracy is a number, not a vibe.

A tracking tag silently dies, a campaign's CPA triples overnight, a feed stops updating, a fraud spike hits — and the first signal is a customer email or a Monday-morning dashboard scroll. By the time someone notices, the damage is days old.

Thresholds and static alerts don't work: they fire constantly on normal seasonality and miss the genuine anomalies that don't cross a fixed line. Statistical and ML anomaly detection (seasonal decomposition, isolation forests, prediction-interval models) catches the real outliers and ignores the noise.

We wire anomaly detection into the pipelines, campaigns, and operational metrics that actually move money, with alerts that land in the channel the responsible team already reads — so problems get triaged in hours, not days.