Pilots that never ship
A working demo with no auth model, no evaluation set, no error handling, and no owner. We define the production bar first, then build to it, so there is no second rewrite.
AI Practice
AI Projects & Automation
We build AI that runs in production and stays there: agents wired into your real systems, retrieval that cites its sources, and automation with a cost ceiling you set before we write code.
What this service actually is
Most AI work that reaches us has already had a pilot. Somebody stood up a chatbot on a set of PDFs, demoed it, and then discovered that the demo could not be trusted with a customer, an invoice, or a regulated record. The gap is rarely the model. It is everything around the model: where the data comes from, who is allowed to see it, what happens on a bad answer, what it costs per thousand requests, and who gets paged when it breaks at 2 a.m.
This practice closes that gap. We take a narrow, high-value workflow and deliver it end to end: data access and permissions, retrieval and grounding, the agent or pipeline itself, evaluation against a labeled set, observability, cost controls, and a rollback path. The output is a system your team can operate, not a notebook that only works on the consultant's laptop.
Because the same practice is CISSP- and CISM-certified on the security side, the threat model is part of the build rather than a review at the end. Prompt injection, data exfiltration through tool calls, over-broad service accounts, and retention of prompts containing customer data are all designed for up front. That combination is the reason clients bring us AI work that touches sensitive records instead of only marketing copy.
Typical engagements land in one of four shapes: an internal agent that removes a repetitive operational task, a retrieval system over a private document corpus, an automation layer that connects systems that were never meant to talk to each other, or a rescue of an AI project that stalled somewhere between prototype and production.
The problems we are usually hired to solve
Answers nobody trusts
Retrieval that returns plausible text without provenance. We build citation-grade retrieval where every claim traces back to a specific document, section, and revision.
Runaway token spend
Unbounded context, retries, and chatty agent loops. We instrument cost per request, cache aggressively, route to cheaper models where quality allows, and set hard ceilings with alerts.
Integration dead ends
The AI works but cannot write back into the ERP, CRM, or ticketing system where the work actually happens. We build the integration path, including the boring parts like idempotency and reconciliation.
Silent quality regression
A model or prompt change quietly degrades output and nobody notices for weeks. We ship a labeled evaluation suite that runs on every change and blocks bad releases.
Security review deadlock
Legal or security blocks launch because nobody can answer where the data goes. We document data flows, retention, and tool permissions in the form reviewers actually accept.
How an engagement actually works
- 01
Workflow selection and baseline
Half a day to a week, depending on scope. We inventory candidate workflows, measure how long each currently takes and how often it is done, and score them on value, data readiness, and failure tolerance. You get a ranked shortlist with an estimated payback for each, and we pick one to build. Choosing the wrong first workflow is the single most common reason AI projects die, so this step is not optional.
- 02
Data, access, and threat model
We map the systems of record, the identities that will call the system, and the data that must never leave a boundary. Service accounts get scoped down, tool permissions get enumerated, and prompt-injection and exfiltration paths get documented with mitigations. This is delivered as a short written architecture and threat note your security reviewer can sign off against.
- 03
Build with an evaluation set from day one
We write the labeled evaluation set before the agent, not after. Every iteration is scored against it, so quality is a number rather than an impression. Retrieval is built with chunking and metadata tuned to your corpus, tool calls are made idempotent, and every response carries the source references behind it.
- 04
Instrument, cap, and pilot
Before real users touch it, the system reports latency, token cost per request, retrieval hit rate, tool-call failures, and refusal rate. Spend ceilings and alerts go in. Then a limited pilot runs with a human in the loop and an explicit escape hatch, so early failures are visible and cheap.
- 05
Handoff and operation
You receive the code, the runbook, the evaluation suite, the dashboards, and a documented rollback procedure. We train the team that will own it. Where a client prefers it, we stay on a defined retainer for model updates, corpus changes, and quarterly cost and quality review, but the system is built so you are never dependent on us.
What you receive
A running system in your environment
Deployed in your cloud tenancy or ours, with source code in your repository and infrastructure defined as code. No black boxes and no vendor lock to us.
A labeled evaluation suite
Test cases drawn from your real data with expected outputs, runnable in CI, so any future prompt or model change is measured rather than guessed at.
Architecture and threat documentation
Data-flow diagrams, tool and permission inventory, retention decisions, and identified injection and exfiltration risks with the mitigation applied to each.
Cost model and controls
Measured cost per request and per workflow run, projected monthly spend at expected volume, caching strategy, and hard spend ceilings with alerting.
Operational runbook
How to deploy, how to roll back, what each alert means, what to check first when output quality drops, and who to escalate to.
A measured before-and-after
The baseline we captured in step one, re-measured after the pilot, so the value of the work is a documented number rather than a claim.
Who this is for
Operations-heavy businesses
Logistics, distribution, healthcare administration, property management, and professional services with high-volume repetitive work that is currently done by people reading and re-keying data.
Teams with a stalled pilot
You have a promising prototype and a security or reliability problem standing between it and production. We are frequently brought in specifically to get that unstuck.
Regulated or sensitive environments
Where an AI system touches records that carry legal or contractual obligations and the build has to survive a real security review.
Frequently asked questions
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