✦Service
Ask your company anything.
Your team's answers are scattered across drives, wikis, manuals and inboxes. We build retrieval-augmented generation (RAG) assistants that search that knowledge, answer in plain language and cite the exact source — while respecting who is allowed to see what. It is the fastest way to make internal knowledge usable.
01What we build
What you get.
- 01
Internal knowledge assistant
A chat interface in your intranet, Slack or Teams that answers staff questions from approved company sources.
- 02
Document ingestion
Connectors for SharePoint, Google Drive, Confluence, Notion, websites and file shares, kept in sync as content changes.
- 03
Search tuned to your content
Hybrid keyword and vector search, sensible chunking and re-ranking, so answers come from the right paragraph.
- 04
Access control
Answers limited to documents each user is permitted to open, mirroring your existing permissions.
- 05
Quality evaluation
A test set of real questions and a scoring process to measure answer quality before and after changes.
02Use cases
Where it pays back.
- 01
Support teams
Give agents instant, sourced answers from product manuals and past tickets during live conversations.
- 02
Manufacturing
Let technicians query equipment manuals, SOPs and maintenance history from the shop floor.
- 03
Legal and compliance
Search policies, regulations and contract libraries, with citations a reviewer can check.
- 04
HR and internal policies
Answer employee questions on leave, benefits and procedures in the employee's preferred language.
- 05
Consulting and research
Surface relevant past proposals, reports and research across years of files.
03Process
How we build it.
- 1
Pick the sources
We choose a focused set of trusted documents and the questions people actually ask about them.
- 2
Build the index
We ingest, clean and index the content with permissions, and set up the retrieval pipeline.
- 3
Evaluate answers
We score answers against a test set and tune search, prompts and content until results are dependable.
- 4
Roll out and maintain
We launch to one team, collect feedback and keep sources in sync as your documents change.
Typical stack
- LlamaIndex / LangChain
- OpenAI / Claude / Gemini APIs
- Open-source LLMs
- pgvector / Qdrant / Pinecone
- Elasticsearch / OpenSearch
- Python / FastAPI
- Next.js
- SharePoint / Google Drive APIs
04Where we work
Teams we work with.
- Dubai & UAEAI chatbots, agents and automation for businesses in Dubai and the UAE — Arabic and English assistants, WhatsApp-first, delivered remotely in your working day.
- Europe & UKAI agents, RAG search and automation for UK and EU companies — privacy-first design with GDPR and the EU AI Act in mind, EU data residency, many languages.
- USAAI agents, copilots and apps for US startups and enterprises — security-minded engineering, HIPAA-aware design and async delivery across US time zones.
- IndiaAI chatbots, document automation and apps for Indian businesses — GST-ready invoice processing, Indian-language WhatsApp bots and DPDP-aware design.
05FAQ
Common questions.
ask us anything else →
01What is RAG, in plain terms?
Retrieval-augmented generation means the AI first searches your documents for relevant passages, then writes an answer based only on those passages. Because the answer is tied to real sources, it is easier to check and much less likely to invent facts than a general chatbot.
02Will our documents be used to train public AI models?
We use API providers and settings where business data is not used for training by default, and we can deploy open-source models in your own cloud if you prefer. We review the provider terms with you before any data is connected.
03Can it respect who is allowed to see which documents?
Yes. We carry document permissions into the search index, so each user only gets answers from material they can already open. This is planned from the start, because adding it later is much harder.
04How do you know the answers are good?
We build a test set of real questions with expected answers and sources, and score the system against it. That lets us compare changes objectively and catch regressions. Users can also flag poor answers, which feed into regular improvements.
05Can it answer in languages other than English?
Yes. The assistant can answer in the user's language even when the source documents are in English, and it can search multilingual content. We test the languages you need with native speakers before rollout.
06More services
Other things we build.
Have a process worth automating?
Tell us about it — we'll reply with how knowledge search (rag) could handle it, what data it needs and a rough plan. The first conversation is free.