01 / ENGINEERING SERVICES
Different tasks need different information and actions. Retrieve document evidence with RAG, query current figures and status through databases and APIs, and connect calculation, validation and workflow tools. Design retrieval paths and tool calls around the question and permissions, then evaluate accuracy, retrieval efficiency and task outcomes with real scenarios.
RAG starts with understanding documents: their structure and how people find, interpret and use them. We design and build data pipelines from source ingestion, cleaning and transformation to search indexes, connecting them to knowledge systems for real workflows.
View scope ↗Combine RAG, databases, APIs and workflow tools into agents built for your business goals. Retrieve information efficiently, then connect calculation, validation and execution through a custom UI.
View scope ↗Assess data and workflow readiness, then progress from a measurable proof of concept to team-owned operations.
View scope ↗DOCUMENT UNDERSTANDING / PRACTICAL RAG
Our approach focuses on turning documents into knowledge people can use at work. We provide document audits, extraction choices and data pipeline design and implementation for ingestion, cleaning and indexing, through user acceptance and operational handover.
01
Analyze hierarchy, tables, footnotes and cross-document references. Understand how users locate sources, compare documents, interpret conditions and make decisions, then translate those patterns into retrieval and answer criteria.
02
Distinguish text PDFs, scans, complex tables and image-heavy documents. Compare direct extraction, OCR engines, layout analysis and vision-based parsing on samples, then combine methods to fit quality and cost requirements.
03
Check that extraction preserves table rows and columns, units, footnotes and conditions. Validate frequent questions and questions where mistakes affect work, distinguishing document cleanup from retrieval improvements.
CAG + RAG / KNOWLEDGE THAT STAYS USEFUL
Cache-Augmented Generation (CAG) preloads knowledge into a long context and reuses cached computation. It is useful for a defined knowledge set that fits the context. Real workflows also need document updates, user permissions and evidence selection for each question.
We evaluate designs that cache stable, reusable knowledge and retrieve current, task-specific evidence. Real work questions guide the choice through quality, latency and total cost, including cache refreshes.
CAG concept: original paperRAG + TOOLS / PURPOSE-BUILT AGENTS
Connect document RAG with database queries, internal APIs, calculation and validation tools. Design agents to select retrieval paths for the question and workflow stage, then use authorized tools for follow-up actions once the required information is available. Tune search scope, repeated calls and context size, evaluating retrieval efficiency alongside task outcomes.
Verify efficiency with real work questions. Measure OCR, indexing and retrieval costs alongside model input tokens and latency, then select a configuration that preserves answer quality.
KNOWLEDGE EVOLUTION / BUILD AS YOU GROW
Start at the stage your work requires. Build on RAG and tool integrations with a lightweight ontology, then expand to a knowledge graph system when long-term knowledge accumulation and relationship queries are needed.
Connect data pipelines, retrieval and workflow tools, then evaluate quality, retrieval efficiency and cost with real questions and task scenarios.
When relationship queries are useful, define shared identifiers, vocabulary and relationships for people, projects, documents and tasks. Configure mappings and a query environment for RDF representation and SPARQL queries, connected to RAG and agents.
Select and deploy a knowledge graph store such as GraphDB based on knowledge volume and query needs. Manage provenance, versions, review status and update, deletion and permission synchronization, continually refining the ontology for agent use.
Choose the adoption stage, store and query environment after reviewing existing systems, real questions and operating costs. Define review criteria for extracted knowledge and responsibility for updates.
02 / LET’S TALK
You do not need a prepared proposal. Our AI consultant helps clarify your work and concerns, and our founder joins the conversation when needed.
Sign in to start your consultation. We will work through the details together as we talk.
03 / FIND YOUR STARTING POINT
Assess your readiness to adopt AI and discover which tasks to prioritize and why. Use your results as the starting point for a follow-up consultation.
Where should our team begin?
Explore your team's goals, bottlenecks, and execution foundation. Discover priority actions and a recommended starting package that match your readiness.
Assess your organizationPeople change before tools spread.
Organizational change begins with individual change.
We help close the AI literacy gap through the AI-Native Enlightenment Movement.
THE NEXT CHAPTER STARTS WITH YOU
Tell us about the work you do today and what you would like to change.
