Our ServicesPlatform & Support
We have shipped AI-powered products, not just AI features: an adaptive interview coach, a multi-model image generation suite for tattoo artists, and a research platform with graph-based data display. That experience is why we start by asking which task the model is actually better at than your current process, and why we design around the fact that models are confidently wrong sometimes.
Included as standard on every engagement — not an upsell list.
We separate the ideas where a language model genuinely wins — summarising, drafting, classifying, extracting structure from messy text, conversational guidance — from the ones better served by a form, a filter or a rule. You get an honest recommendation, including where the answer is not to build anything.
Assistants answer from your documents, product data, policies and knowledge base rather than from general training data, using retrieval over your own content. That is what makes the difference between a support assistant that quotes your actual returns policy and one that invents a plausible one.
Internal tools that draft product descriptions in your tone of voice, summarise long enquiry threads, triage and route incoming leads, translate and adapt copy across languages, or extract structured fields from uploaded documents into your database.
We are not locked to one vendor. Model choice is made per task on capability, latency and cost, with the provider abstracted behind our own interface so switching does not mean rewriting the product. InkGenX runs multiple image models behind a single workflow for exactly this reason.
Versioned prompts with structured output schemas, a test set of real inputs to compare changes against, refusal and fallback handling, output validation before anything is saved or shown, and a human review step wherever a wrong answer would carry real consequences.
Token budgeting, caching of repeated queries, streaming responses so the interface feels immediate, request throttling per user, and usage dashboards. AI features fail commercially far more often than technically, and the cause is almost always an unmodelled per-request cost.
We agree what may be sent to a model provider, what must be redacted, retention and training-opt-out settings, and where processing happens, then document it — so your privacy policy reflects reality and your customers get a straight answer when they ask.
Stage by stage, with the approval points marked. AI & LLM Integration follows the same rhythm on every project.
We define the job to be done, what a good output looks like, and what the current process costs in time or accuracy. Without a baseline there is no way to tell whether the AI feature is an improvement or just novel.
We build a narrow prototype using your genuine content and awkward real-world inputs, not curated examples, and put it in front of the people who will use it. Most concepts change materially at this stage, which is exactly what the stage is for.
Retrieval and chunking strategy, prompt templates, structured output parsing, validation, fallbacks and logging are built as a proper pipeline rather than a single call, so behaviour is inspectable and each part can be improved independently.
A fixed evaluation set of real inputs is scored before and after every prompt, model or retrieval change, so improvements are demonstrated rather than felt. We tune retrieval quality first, since most bad answers come from bad context rather than a weak model.
Launch includes usage and cost monitoring, feedback capture on individual responses, and a review cycle where real conversations drive the next round of prompt and retrieval improvements.
The concrete artefacts handed over at the end — files, access and documentation you keep.
If two or three of these sound like your situation, this is the right place to start.
Standard, portable tooling. The licences, accounts and source stay in your name, so nothing here is a reason you cannot leave.
Live projects where this work did the heavy lifting:
Work that usually runs alongside this one:
What we have written about this, in more depth than a service page allows:
The questions we get asked most about AI & LLM Integration.
Send us the brief — or just the problem. You will get a written scope, a timeline and a fixed price, usually within one working day.
