Production AI, past the proof of concept.

We spend two to three weeks inside your workflows, data, and systems, then score the candidate use cases on feasibility, payoff, and risk. You get a roadmap your engineers can run with, plus a written list of the ideas we'd skip and why. Often only one thing is worth building right now. The assessment tells you which one.

We grade against the same standards we hold our own production systems to, from evaluation pipelines and data readiness to permission boundaries and fallback logic.

For: Leadership teams who need a plan their engineers can start Monday

We take one or two priority use cases and spec them to the point where an engineering team can start building. That covers architecture, model selection across Claude, OpenAI, Gemini, and open-weight options, data and integration requirements, and an evaluation plan with accuracy targets set before any code gets written. Most teams run this after the Readiness Assessment, but it works standalone if you already know what you want to build. The spec is yours, whether we build it or your own engineers do.

For: CTOs and product leads who know the use case but aren't ready to commit to a build

We pick one contained workflow with you, build the agent on whichever model fits it, wire it into your systems, and ship it with permission boundaries, fallback logic, and a record of every action it takes. Four to six weeks later you have a working agent in production and the numbers to decide whether to expand. If the process you want to automate spans several systems and approval steps, look at AI Workflow Automation below instead.

Relate AI, our relationship operating system, runs agents with configurable autonomy. They can suggest only, draft for approval, or auto-run a narrow set of low-risk updates.

For: Engineering and ops leaders with one well-defined workflow to automate

This one's for teams shipping an AI product of their own. We build a working prototype in the first few weeks to test whether the core interaction holds up, then build the production version around it, with data pipelines, model orchestration, an evaluation harness, and the infrastructure to run it. The point of the prototype is to surface bad assumptions while they're still cheap to fix.

One of our own products went from first prototype to a revenue-generating consumer marketplace, and we still operate it in production today.

For: Founders and product teams shipping an AI-native product

We build pipelines for the documents your team handles by hand, including scanned PDFs, inconsistent formats, and the requirement that matters buried on page forty. They parse, extract the fields you care about, validate against your rules, and send anything low-confidence to a human review queue. What comes out the other end is structured data flowing into your systems, with a record of what the model decided and why.

Vercor, our RFP response system, parses messy government and enterprise procurement documents, extracts structured requirements, and runs a compliance validation pass before human review.

For: Operations teams buried in contracts, invoices, or compliance documents

Your company already wrote the answer. It's in a doc from three years ago that nobody can find. We build retrieval-backed assistants grounded on your wikis, tickets, policies, transcripts, and shared drives. Every answer cites its sources, and permissions follow your existing access model, so nobody sees content they couldn't already open. Retrieval is only as good as the sources under it, so the engagement includes cleaning up the corpus itself. We measure accuracy against a labeled question set before launch.

Vercor drafts proposals from a knowledge base of prior proposals, case studies, resumes, and policy documents, broken into retrievable units so the right evidence reaches the right section.

For: Teams whose institutional knowledge is scattered across drives, wikis, and inboxes

We automate the whole RFP loop, from pulling requirements out of the source documents through drafting, compliance checks, and assembly. Drafts come from a knowledge base of your past proposals and resumes, and a validation pass flags gaps before a reviewer reads a word. We sell a product that does exactly this, so you're starting from patterns that already work in production. If you only submit a few proposals a year, doing it by hand is still cheaper.

Vercor runs an 8-stage pipeline in production: Upload, Parse, Extract, Outline, Draft, Assemble, Format, Validate.

For: Government contractors, grant-funded organizations, and professional services firms with steady RFP volume

We run forecasting as a scoped pilot. First we check whether your warehouse history can support a reliable model, and tell you what to fix if it can't. Then we train and backtest models for demand planning, churn risk, or capacity calls against held-out history, benchmarked against however you forecast today. If the model can't beat your current method, we won't ship it. The dashboards get built for the operators who'll use them day to day.

The data plumbing underneath is proven work. We built Snowflake-backed pipelines for bulk operations across massive datasets at Discord, and a real-time analytics portal serving Burger King franchise operators across thousands of locations.

For: Finance and operations leaders making volume, inventory, or staffing calls

We build the labeling interfaces, review queues, and pipelines that move raw sources into training-ready sets, plus the warehouse-scale plumbing for bulk operations on big datasets. If a fine-tuning effort has stalled, the data loop is usually why, and this is the engagement that fixes it.

For Discord, we built ML training and data labeling tools for content classification. The result was higher-quality training data flowing to ML pipelines continuously and improved model accuracy for detecting harmful content.

For: ML and data teams training or fine-tuning their own models

A support assistant wired into your help content, order systems, and account data, so it can check an order, issue a credit, or update an address on its own. We measure containment against an evaluation suite before launch, and the moment confidence drops it hands a human the full conversation. Chat ships first. Once the numbers hold up, voice and IVR replacement come in as a second phase.

The grounding discipline comes from our own products. Vercor drafts from a structured knowledge base and runs a validation pass before a human ever reviews it.

For: Support and CX leaders deflecting repetitive chat and email tickets

Copilots for your reps. They draft replies from the ticket thread and your policy docs, summarize long histories for handoffs, and surface next steps inside the tools your team already uses. Then analytics on top of it, covering what customers keep asking, where reps get stuck, and which answers close tickets.

Our own Relate AI drafts replies from the live thread, so reps just approve and send.

For: Contact center and support ops leaders with a human team to keep

The work that eats your team's week is usually a chain of small decisions over messy inputs. We map that chain, automate the steps a model can own, and leave humans on the approvals that carry risk. Everything wires into your existing CRM, ERP, and warehouse, and every automated decision leaves an audit trail. This is the bigger build, for processes that span several systems and approval gates. If you'd rather prove the value on one contained task first, start with the First AI Agent Build.

For Discord's Trust and Safety org, our tooling cut manual overhead on bulk user actions by 40 percent.

For: Ops and RevOps leaders with repetitive multi-step processes

Admin panels, review queues, and operations dashboards where AI handles the tedious work of classifying records, summarizing cases, drafting responses, and flagging anomalies. We're a Retool development agency and Preferred Partner, so the low-code path moves fast, and we write custom React when a tool outgrows it.

We delivered a franchise analytics portal for Burger King within two months, serving operators across thousands of locations on Retool, React, and Snowflake.

For: Teams running operations out of spreadsheets and aging admin panels

Some internal work is easier to run as a conversation than a dashboard. We build MCP servers that expose your operations as typed tools inside ChatGPT or Claude. User lookups, refunds, reporting, moderation, whatever your team does all day, each behind layered auth with role checks. Part of the deliverable is a written take on which of your workflows belong in chat and which should stay a dashboard.

Our own ChatGPT app, an MCP server on Cloudflare Workers with React widgets, replaced the admin dashboard for the user lookups, refunds, and moderation work our operators run daily on one of our consumer products.

For: CTOs and ops leads weighing chat against another internal dashboard

You have a prototype that works in the demo channel. Production asks harder questions, like what happens when the model is wrong, who pays the inference bill, what gets logged, and who can see what. We take the prototype, yours or another vendor's, add an evaluation suite, fallback logic, rate and cost controls, and observability, then deploy it into your cloud. The first step is a short review that settles whether it should be hardened or rebuilt, so you don't spend on the wrong path.

Vercor runs multi-tenant on Cloudflare and was built for spiky, document-heavy demand, with deliberate answers on document handling, access control, and data separation.

For: Teams with a promising internal demo that never shipped

Models get deprecated, providers change pricing, and prompts that worked in March quietly degrade by June. We run your AI systems month to month, with evaluation regressions on every model or prompt change, drift monitoring, migrations when a deprecation lands, and a monthly report on accuracy and spend. You get an AI automation agency on call without hiring an ML platform team. Coverage is business hours with agreed escalation tiers, and there's a three-month minimum to start.

Our own products run under the same regime, and when something breaks we publish what happened and how we fixed it.

For: Companies running production AI without a dedicated ML platform team

We run fixed-scope pilots on document-heavy agency workflows like records request triage, responsive-document search with redaction review queues, case file summarization, and intake classification. Everything deploys in your environment with role-based access and a complete record of every model action, and a human approves anything that goes out the door. Accuracy gets measured against criteria your counsel and records officers help define. The timeline covers build and deployment; agency-side procurement and security review add their own lead time, and we plan around it. Our size works in your favor here, since the engineers who scope the pilot are the ones who build it.

Our production auth pattern layers a one-time-password flow, short-lived signed tokens validated on each call, and database role checks on sensitive actions, so a stale token never grants access a user no longer has.

For: Agency innovation, operations, and IT leaders, plus records officers and counsel