The hard part was never the model.
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.
Which task would you hand to a language model first, and how would you know it worked?
Thirty minutes with the engineer who builds these integrations. No slide deck.
Six reasons the pilot never ships.
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.
- 01 It works on the five examples someone tried and fails on the sixth
- 02 The output format changes just enough to break whatever reads it
- 03 Nobody agreed what good looks like, so nobody can say if it is ready
- 04 Every request goes to the most expensive model because that was the demo
- 05 The bill is fine at pilot volume and alarming at real volume
- 06 It lives beside the workflow, so using it is extra work
What goes into an XtractSol integration
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.
The right one, not the famous one
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.
Inside the workflow, not beside it
Connected to the tools your team already uses, so the benefit arrives without anyone changing how they work or opening another tab.
Consistent output, not clever output
The instructions behind the scenes designed and refined against your real cases, so the model gives you the same useful shape every time.
Checked before anything acts on it
Responses validated against the structure and rules the task requires. What fails gets retried or sent to a person, never passed downstream as data.
No surprises on the bill
Each task routed to the cheapest model that clears your quality bar, with usage tracked, so spend scales sensibly as adoption grows.
Held to your standards
Access controls and data handling built to the requirements you are held to, agreed in writing before the build rather than bolted on later.
Where the money actually goes
Most workloads are mostly easy. A few requests genuinely need the strongest model available, and the rest do not. Sending all of them to the same place is the most common waste we find.
Twenty-eight calls, which is well under one percent of the day, account for more than a quarter of the bill. That is not a problem in itself, because those twenty-eight genuinely needed the capability. The problem is what happens when the other four thousand are charged at the same rate, which is what a single default model does quietly, every day, until somebody reads the invoice.
The model is one step in the middle, not the whole thing.
Most of the reliability comes from what happens either side of it.
Route
The task is classified and sent to the cheapest model that can do it.
SystemAssemble
Context pulled from your systems so the model is not working blind.
SystemGenerate
The model responds, inside instructions tuned for this specific job.
SystemValidate
Output checked against the structure and rules the task requires.
SystemRetry
What fails is retried, and what keeps failing goes to a person.
SystemDecide
Anything consequential still gets a human before it counts.
Your teamWhatever you pick today will be the wrong choice within a year
Stanford'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.
Chosen for the job, connected to your stack
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.
You see the quality and the cost before you scale it
We learn what you are trying to accomplish and what it has to live up to, then build in stages you can inspect.
Discovery call
The task you want handled, and what good output actually looks like for it.
Workflow audit
The task, your systems and your privacy needs, with honest expectations set on quality, cost and effort.
Build and tune
Prompts and validation refined on your real cases, measured against an agreed quality bar.
Deploy and optimise
Live inside your tools, then tracked on quality and cost against real usage and tuned from there.
Which language models do you work with?
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.
What happens when a better or cheaper model launches?
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.
Should we build our own model or integrate an existing one?
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.
What if the model returns something wrong or malformed?
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.
How do you keep quality and cost under control?
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.
What does it cost?
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.
Ready to get started?
Book a free discovery call and we will map how agentic AI can fit your workflows.