It is everything around it. We build the layer that makes the output reliable: the right model for each task, connected to your systems, with checks on what comes back and a close eye on the bill.
Thirty minutes with the engineer who builds these integrations. No slide deck.
There is a wide gap between a language model impressing somebody in a browser tab and it doing a real job every day without supervision.
Each integration is built around the task you need done, the systems you run and the standards you have to meet. Start with one use case or build out several.
There is no single best model, only the best one for your task, budget and privacy needs. We help you avoid paying for capability the job does not need.
Connected to the tools your team already uses, so the benefit arrives without anyone changing how they work or opening another tab.
The instructions behind the scenes designed and refined against your real cases, so the model gives you the same useful shape every time.
Responses validated against the structure and rules the task requires. What fails gets retried or sent to a person, never passed downstream as data.
Each task routed to the cheapest model that clears your quality bar, with usage tracked, so spend scales sensibly as adoption grows.
Access controls and data handling built to the requirements you are held to, agreed in writing before the build rather than bolted on later.
Most of the reliability comes from what happens either side of it.
The task is classified and sent to the cheapest model that can do it.
SystemContext pulled from your systems so the model is not working blind.
SystemThe model responds, inside instructions tuned for this specific job.
SystemOutput checked against the structure and rules the task requires.
SystemWhat fails is retried, and what keeps failing goes to a person.
SystemAnything consequential still gets a human before it counts.
Your teamStanford's AI Index recorded the cost of GPT-3.5 level capability falling from $20 to $0.07 per million tokens in under two years, and the gap between open-weight and closed models narrowing from 8% to 1.7% on some benchmarks in a single year. Nothing in that picture suggests the model you choose this quarter stays optimal. Any integration built as though it will is a rewrite waiting to happen.
So we keep the parts that took the work, the prompts, the validation rules and the evaluation set, separate from the provider. Swapping a model becomes a configuration change and a re-run of your test set, not a rebuild. You get to take advantage of a better or cheaper option when it appears, which historically has been roughly every few months.
Sending every request to the most powerful model is not a quality strategy. It is just the demo, left running with a bill attached.
We work across commercial and open-weight models, hosted wherever your privacy requirements point, and connect them to the tools you already run.
Model availability, pricing and capability move quickly, and hosting options vary by region and provider. We confirm what fits your privacy and budget requirements during the workflow audit.
We learn what you are trying to accomplish and what it has to live up to, then build in stages you can inspect.
The task you want handled, and what good output actually looks like for it.
The task, your systems and your privacy needs, with honest expectations set on quality, cost and effort.
Prompts and validation refined on your real cases, measured against an agreed quality bar.
Live inside your tools, then tracked on quality and cost against real usage and tuned from there.
We work with the major commercial and open-weight models and choose based on your task, budget and privacy needs rather than pushing one option. Part of the value we add is matching the right model to the job instead of defaulting to whatever is popular this quarter.
You switch, and it is a configuration change rather than a rebuild. We keep the prompts, the validation rules and the evaluation set separate from the provider, so swapping a model means re-running your test set and comparing the numbers. Given how fast pricing and capability have moved, we treat that as a certainty rather than a possibility.
For almost every business, integrating and tuning an existing model is faster, cheaper and more than good enough. Training from scratch is rarely justified. We would rather get you a working result without the over-engineering, and we will tell you on the call if we think your case is the exception.
Output is validated before anything downstream sees it. Responses are checked against the structure and rules the task requires, and anything that fails is retried or routed to a person rather than passed along. The point of the validation layer is that a bad response becomes a caught exception instead of bad data in your system.
Quality is measured against a test set built from your real cases, so changes are compared rather than guessed at. Cost is controlled by routing each task to the cheapest model that clears your quality bar, rather than sending every request to the most expensive one. Once live we monitor both and keep tuning as usage grows.
A build fee for the integration plus a monthly fee to run and maintain it. Model usage is billed at cost and shown to you separately, so you always know what you are paying the provider and what you are paying us. You get a firm number after the workflow audit.
Tell us the task and the systems it touches. We will tell you which model tier it actually needs, roughly what it will cost to run, and whether it is worth doing at all.
