Generative AI Applications
Assistants and search that answer from your own documents and show where each answer came from.
Why It Matters
Most AI pilots end as a chat window that few people use. The work it promised still waits for a person who knows where to look.
A general model does not know your products, your contracts or your rules. When it answers from what it learned elsewhere, the answer sounds right and is wrong, and people stop trusting it. The common failures are a pilot with no owner, answers that cannot be checked, and a bill that grows with every user.
What We Do
Assistants and Copilots for Staff
A copilot inside your existing tools that drafts, summarises and finds answers, with the source shown and access checked before each answer.
Knowledge Search Over Your Documents
Search and answers over your own documents, with the passage and document name beside every answer so a reader can check it.
Content Generation
Draft text and material in your voice from your own approved sources, with people reviewing before anything is published.
Customer Chat
A chat service that answers common questions from your own content, completes simple requests and passes the rest to your staff with the history.
Testing and Guardrails
A test set built from real questions, a pass mark agreed with you, and limits that keep answers on topic and refuse weak evidence.
What Changes for You
Answers With Sources
Every answer names the document and the passage behind it, so a reader can check it in seconds.
A Product People Use
The assistant sits inside the tool your staff already use, and the first release solves one task well.
Quality You Can See
A test set of real questions scores each release, and a short report shows accuracy, refusals and cost.
How the Work Runs
The work starts with one task and one group of users. Each stage ends with a decision you make before the next one begins.
Choose the Job
We pick one task with a clear cost, such as finding a clause or drafting a reply. You name the users and the decision they need to make, and we agree how success is measured.
Connect the Sources
We list the documents and systems the answer may use. You confirm who may see each source, and your security team approves the access before we build.
Build the Product
We build the assistant, the search and the screen where people use it. Your team reviews the first version and decides which answers are acceptable.
Measure the Answers
We test against real questions with known answers. You agree the pass mark, and nothing is released below it. A weak answer is refused, not hidden.
Run and Improve
We watch quality, refusals and cost after release. Your team adds questions to the test set and decides which improvements come next.
Teach the Team
We train your staff to add sources, change prompts and read the quality report, so the product keeps improving with your own people.
Platforms and Technologies
We build on the model services and cloud platforms you already approve. Google Cloud provides the managed services, and NVIDIA GPUs are the processors that run large models. They serve any open-weight model you host yourself. OpenAI, Anthropic Claude and Google Gemini are hosted model services; Azure OpenAI, Amazon Bedrock and Google Vertex AI run models inside those cloud accounts. Meta Llama, Mistral and Qwen are open-weight models you can run in your own environment. LangChain and LlamaIndex are libraries that connect a model to your documents and tools. Retrieval augmented generation is the step that finds the passages before the model writes, and every answer keeps a link to them.
- AI and machine learning
- OpenAI GPT
- Anthropic Claude
- Google Gemini
- Meta Llama
- Mistral
- Qwen
- Azure OpenAI
- Amazon Bedrock
- Vertex AI
- LangChain
- LlamaIndex
- Methods We Use
- Retrieval augmented generation
- Evaluation sets built from real questions
- Source checks and refusal
- Model use and GPU cost control
Naming a product does not mean a partnership with its vendor.
Industries
Banking and Financial Services
Banking, payments, lending, wealth, insurance and digital wallets: the platforms they run on and the connections between them.
Media and Telecommunications
Consumer platforms, streaming, connected devices and ad-tech that stay fast at peak audience, with AI for content and personalisation.
Retail and Omnichannel Commerce
POS integration, stock and price sync, card payments, loyalty and e-commerce for retailers with many sites.
Questions
What buyers ask before they build a generative AI product.
How is this different from a general AI tool?
A general tool answers from what it learned in training. Our assistants read your documents, respect your access rules and show the passage behind each answer. Your team keeps the sources and the test set.
What drives the running cost?
The number of users, the length of the documents and the model each task uses. We send routine questions to a smaller model and measure the cost per answer, so you see what each release adds.
How do you stop wrong answers?
We build a test set from real questions and score every release against it. The assistant answers only from the sources you allow, refuses when the evidence is weak, and shows the passage so a reader can check.
Where does our data go?
Where you choose. The models and the document index can run in your cloud account or inside your own environment, under your access rules. We agree the boundary before the first document is loaded.
Who owns what you build?
You own the application, the prompts, the test set and the index we build for you. Model licences stay with their providers, and we list them in the handover.
Tell Us What You Need to Run, Build or Secure.
Describe the service, the systems and the pressure you are under. The right team takes it from there.