LLMs development
Language models, kept on a short leash.
A language model is very good at sounding right, which is exactly the danger. Useful ones are grounded in your material, cite where an answer came from, and are allowed to say they do not know.
Overview
LLMs, the way we do it
The interesting question about an LLM is never whether it can answer. It is whether it will answer wrongly and confidently in front of a customer, and whether anyone would notice. A model with no grounding will invent a policy, a price or a part number in a tone indistinguishable from the truth.
We build them against your own documents, with the source shown next to the answer, and a boundary on what they will attempt. Cost is measured per request before you commit, because the bill for a chat interface is a function of how many people use it and how long the answers are.
What's included
What you actually get
- Answers grounded in your documents, with the source shown
- A defined boundary, so it declines rather than inventing
- Runs on infrastructure we manage, or a provider contractually barred from training on your content
- Cost per request measured before commitment, with a monthly ceiling
- Evaluated against real questions from your business, not a demo set
How we work
Five steps, no surprises
- 01
We talk
A call or a WhatsApp thread. You tell us what is not working; we tell you honestly whether we are the right people.
- 02
We scope it
A written plan with what you get, what it costs and how long it takes. Fixed, so there are no surprises later.
- 03
We build it
You see it as it goes, not at the end. Changes are cheap while it is still being built.
- 04
We put it live
On infrastructure we set up and secure, tested before the launch date.
- 05
We keep it running
Updates, monitoring and someone who answers. Most clients stay on a monthly agreement.
Questions
About llms
What stops it making things up?
Grounding it in your own material and having it cite the source, so any answer can be checked. And a limit on what it is allowed to answer at all: a system that declines is more useful than one that answers incorrectly.
What will it cost to run each month?
It scales with use, so we measure it per request before you commit and give you a projected figure with a ceiling, plus monitoring that tells us if real usage drifts above it.
Which model would you use?
Whichever fits the job, the budget and your data rules. That is a decision to make with the requirements in front of us, not one to fix in advance.
Also
Related work
- Quality AssuranceFind it before your customers do.
- UX DesignWork out what it should do before anyone builds it.
- UI DesignScreens people can use without being trained.
- Design SystemsOne set of rules, so the tenth screen looks like the first.
- Mobile ApplicationsApps people keep on the first screen.
- iOSiPhone apps built the way Apple expects.
- AndroidAndroid apps that work on the phones people actually own.
- FlutterFlutter, when one team has to cover both stores.
- AIAI that does one job, properly.
- Machine LearningModels that earn their place in the business.
- Data ScienceAnswers from the data you already have.
- Generative AIGeneration with a human still holding the pen.
- PythonPython, for the work that has to be read as well as run.
- Back-EndWhere an order becomes an order.
- DatabaseThe part of your system that is hardest to fix later.
- Node.jsNode.js, for the systems that have to answer quickly.
- GoGo, where it has to be fast and stay simple.
- .NET.NET, for the systems a business runs on.
- JavaJava, for systems measured in decades.
- Front-EndThe half of your product people actually see.
- Web DevelopmentCompany sites, customer portals, and web systems that hold up.
- ReactReact, built so the next team can still work on it.
- AngularAngular, for systems that have to last.
Tell us what you have in mind.
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