Performance funnel rebuild
A B2B services company needed acquisition it could trust. We fixed tracking first, then scaled Meta and Google against true cost per acquisition.
- Location
- Remote / EU
- Project
- Performance Marketing
- Industry
- B2B Services
Answersgroundedinyourknowledge.
RAG systems, chatbots, and voice AI — built so responses cite your content, with memory layers that stay coherent over time.
Syed Wajeeh ul Hassan
Harris Khan
Bassam HashmiWorking with businesses across the US, UK, Europe and Australia
Automate — AI & Engineering
Retrieval first.Then conversation.
We clean and chunk source knowledge, tune retrieval, and only then wrap a conversational or voice interface — so hallucination risk stays managed.
- Knowledge prep and retrieval evaluation
- Chat and voice interfaces with clear handoff to humans
- Memory and session design for multi-turn quality
How we work
Build once.Improve continuously.
- 01
Discovery
We learn what you sell, who buys it and what is working now. No proposal before this conversation.
- 02
Diagnosis
We audit traffic, tracking, funnel and systems, then name the real constraint on growth.
- 03
Plan
A scoped plan with clear deliverables, timelines and the numbers we will be judged on.
- 04
Build & Scale
We execute in the open, report on what matters, fund what is proven and cut what is not.
Case Studies
Work measured by outcomes,not activity reports.
Support automation system
An online retailer needed to scale customer support without adding headcount. We automated triage and drafted replies with brand context built in.
- Location
- Remote / US
- Project
- AI Automation
- Industry
- E-commerce
Development
Conversion site
Digital Marketing
Lifecycle campaigns
AI Automation
Ops automation
Client feedback
Technology is not the objective.Performance is.
“They redesigned our internal workflows with automation and delivered measurable efficiency, without overselling what AI could do.”
Michael Carter
Chief Technology Officer
“Support operations became dramatically more efficient after the conversational system went live. Clear scope, honest reporting.”
Sophia Bennett
Head of Operations
“They tied paid spend to pipeline we could actually trust. The monthly report finally answered whether the marketing made money.”
Daniel Hughes
Director of Growth
FAQ
Questions about RAG Systems & Conversational AI
RAG is a way of making a language model answer from your own information. Before answering, the system retrieves the relevant passages from your documents or data and gives them to the model, so answers are grounded in your content and can cite sources.
PDFs, Word documents, help centre articles, websites, databases, CRM records, ticketing systems and internal wikis. If it can be read, it can usually be indexed.
Yes. The same knowledge base can power web chat, WhatsApp, in-app assistants and voice agents for phone lines. Voice adds speech-to-text and text-to-speech layers on top of the retrieval system.
A memory layer lets the assistant remember context across conversations: who the user is, what they asked before, what was resolved. It makes the assistant useful for returning customers and internal teams rather than starting from zero every time.
Accuracy depends on the quality of the source material and the retrieval setup. We test against a set of real questions before launch, measure how often answers are correct and cited, and keep tuning until the number holds.
Ready to talk about RAG Systems & Conversational AI (Voice AI, Chatbots, Memory Layers)?
A 30-minute call. We will look at your current setup and tell you where the biggest gap is — whether or not you work with us.