ServicesCore practice
Generative AI Solutions
Between 'we tried ChatGPT' and 'AI runs this workflow' sits real engineering: choosing and fine-tuning the right model, grounding it in your data, measuring its quality, and governing its cost. We take generative AI use cases — drafting, summarisation, extraction, clinical reporting, content pipelines — from idea to a system your team relies on daily.
That includes LLM fine-tuning with LoRA, QLoRA and DPO where prompting is not enough, document AI pipelines for layout, table and figure extraction, and LLMOps: prompt and index versioning, offline and online evals, gold-set regression tests, and gated rollouts that catch quality drift before release. It also includes spend: one platform cut LLM cost 35% with no measured quality regression through caching, model routing and prompt compression.
What's included
Named deliverables, not vague verbs
LLM fine-tuning
LoRA, QLoRA, PEFT and DPO — applied when the eval evidence says prompting alone falls short.
Document & multimodal AI
OCR, layout, table and figure extraction across formats; vision-generated captions that make images retrievable.
Evaluation & quality gates
Gold-set regression suites and online evals, so a prompt change is tested like a code change.
Cost governance
Semantic caching, tiered model routing, prompt compression, daily budget caps — spend engineered, not discovered on the invoice.
AI feature integration
Generative capability embedded in your existing product, with structured outputs your code can trust.
The evidence
Where we've done this before
Stack for this practice
What it's built with
- Azure OpenAI
- OpenAI
- Anthropic Claude
- Llama
- LoRA / QLoRA / DPO
- LangChain
- Azure AI Foundry
- LangSmith
- Ragas
- Airflow
- Kafka
Evaluator questions
Asked by every serious buyer
Fine-tune or prompt?
Measured, not guessed: we build the eval set first, try the cheap approach, and fine-tune only when the numbers say so. Fine-tuning you didn't need is cost and maintenance you can't get back.
How do you keep quality from degrading over time?
Every prompt, index and model choice is versioned and regression-tested against a gold set before release — the same discipline as shipping code. Quality drift gets caught in the gate, not reported by your users.
Ready to scope generative ai for your business?
Thirty minutes with a founder. You leave with an honest read on feasibility, a first-step recommendation, and no obligation.