Fix your Data Retrieval Pipeline
We diagnose and fix RAG and document pipelines so AI systems can pass internal reviews, scale beyond pilots, and be deployed with confidence.
From broken pipelines to production-ready AI, simplified.
Looks Easy. Breaks Under Pressure.
Halluctinations
Incomplete retrieval
Chunking that doesn't match structure
Slow responses and high latency
[ RetrieveIQ - Production-ready ]
RAG Pipeline Audit
Failure Analysis • Retrieval Quality • Relevance Scoring
ETL Pipeline Repair
Data Validation, Error Handling, Pipeline Monitoring
Pipeline Engineering
Architecture Design, Documentation, Best Practices
Engineered, Observable, Reliable
Validated outputs
Safe pipelines
Tracked updates
Pipeline Services
Retrieval & Chunking Audits
Focused review of how your data is chunked, embedded and retrieved. Identify why relevant information is being missed or duplicated.
ETL Pipeline Review
Review ingestion and transformation pipelines that feed your RAG system. Ensure data is clean, structured and updated in ways retrieval systems can actually use.
Hybrid Search
Design and tune hybrid search systems combining dense and keyword retrieval. Balance recall, precision and latency for real production queries.
Prompt & System Design Fixes
Fix prompt and system level issues that compound retrieval failures. Reduce hallucinations caused by missing or poorly surfaced context.
Cost & Latency Optimisation
Target the slowest and most expensive parts of the pipeline. Reduce end-to-end response time and model spend without major rewrites.
We Help Teams Eliminate Hallucinations and Poor Retrieval in RAG Systems
We diagnose and fix retrieval augmented generation systems that don't perform as expected. Improving grounding, retrieval relevance, latency and cost efficiency.
Attach
We integrate with your existing retrieval and LLM stack.
Production Readiness
Leave you with a faster, cheaper and measurable system
Get Production RAG Under Control
A framework that helps teams move from demo grade RAG to production ready systems.
The Engine Behind AI
LangChain
TensorFlow
PyTorch
Zapier
Make (Integromat)
n8n
Python
Google Sheets
Airtable
Notion Database
PostgreSQL
ChatGPT
HubSpot
Python
Cloudflare
Datadog
Vercel
AWS
Twilio
Gmail
Intercom
Case Studies
Improving Contract Review Accuracy for a LegalTech SaaS Platform
We helped a growing LegalTech SaaS platform increase RAG retrieval accuracy from 60% to 92%, enabling a successful firm-wide pilot rollout to BigLaw clients and unlocking revenue-generating deployments. The engagement focused on identifying structural failures across the ingestion and retrieval stack. We rebuilt core ETL and retrieval logic, refined chunking strategies, upgraded embedding configurations, and corrected prompt-level grounding issues. These changes significantly reduced hallucinations and ensured responses consistently surfaced legally relevant context. As a result, the client moved from a stalled proof-of-concept to a production-ready system that met the reliability thresholds required by enterprise legal teams.
92% Retrieval accuracy
40% Less hallucinations
2-week build time
Restoring Grounding and Source Attribution for University Lecture Content
A Higher Education client was experiencing unreliable LLM outputs due to missing or inconsistent source attribution across lecture materials. This created issues with trust, academic accuracy, and internal adoption. We identified the root cause within the ETL and chunking pipeline, then redesigned chunk boundaries and attribution guardrails to ensure responses consistently referenced the correct source material. Prompt-level constraints were introduced to prevent unsupported claims and improve traceability. Following these changes, the system produced grounded, auditable responses suitable for academic use and internal rollout.
100% Source Attribution Coverage
2-week build time
No Surprises. Just Clarity.
Your Questions, Answered
Here are answers to the questions we get most often about our projects, pricing, and process.
Coming Soon
Ready to Optimise your Retrieval Pipeline?
Get in touch with us.