Discover & Scope
Align on problems, data reality, and success metrics. Opportunity brief, KPI model, phased roadmap, effort/cost ranges.
Data Engineering &
RAG Platforms
Make your documents, tickets and systems of record actually useful — with retrieval that cites its sources and a pipeline that does not fall over when the format changes.
Search that understands context, backed by pipelines that do not break
Most enterprise RAG projects fail at the ingestion pipeline, not the LLM. If your chunking strategy ignores document layout, if your parser drops table structures, or if your vector index goes out of sync with your source database, your model will hallucinate regardless of which LLM you pick. We build production RAG on top of robust, battle-tested data pipelines. We handle complex layout parsing (tables, diagrams, PDFs, scanned documents), multi-stage hybrid search (dense vectors + sparse keywordBM25), and metadata filtering so the model only searches what the user actually has permission to see.
Crucially, every answer returned by our retrieval systems includes explicit source citations down to the paragraph or page number, allowing users to verify facts in one click. We set up automated re-indexing pipelines that listen to your source systems (Confluence, SharePoint, SQL databases, S3 buckets) and update vector embeddings in real time as files are modified or deleted.
What We Do?
We architect and deploy enterprise-grade retrieval-augmented generation (RAG) platforms and data pipelines. We build multi-modal ingestion engines capable of processing raw documents, tickets, logs, and database records into structured, queryable knowledge graphs and vector databases.
We implement security-first retrieval with fine-grained role-based access control (RBAC), custom re-ranking models, and continuous synchronization with your internal systems of record.
Key Deliverables
A production-ready RAG platform connected to your enterprise data sources, delivering verifiable responses with built-in security and automated data syncing.
- Multi-stage hybrid retrieval engine combining vector embeddings with keyword search
- Automated ingestion pipeline with layout-aware PDF, table, and document parsing
- Role-based security layer ensuring retrieval respects source access permissions
- Citation and source-attribution UI integration with page-level verification
What's included ?
- + Data audit and ingestion pipeline setup
- + Vector database selection, configuration, and indexing
- +Hybrid search, re-ranking, and retrieval optimization
- + Permission sync and enterprise security integration
Process
From Idea
to Production
to Production
FAQs
Frequently
asked questions
asked questions
A focused pilot reaches a working, testable v1 in four to six weeks. A production system - with on-site deployment and integration-typically runs eight to twelve weeks depending on hardware, data access and how many systems it has to talk to. We tell you which one you are in during discovery, not after.
A clear problem statement, a definition of success, access to sample data, and one stakeholder who can make decisions. That is genuinely it. We run a kickoff workshop to pin down scope and the KPI model before anyone writes code.
Whichever ones wins on accuracy, latency and cost for your problem. In practice: Claude and GPT for reasoning and generation; Llama and Mistral when it has to be self-hosted; YOLO, OpenCV and InsightFace for vision; PyTorch, ONNX and TensorRT for anything on the edge. We are not loyal to a vendor. We are loyal to the benchmark
Yes, and several of our systems do. We have shipped fully on-premise vision analytics that runs a single executable with no internet connection, and self-hosted voice assistants where every inference endpoint stays inside the customer VM. If compliance rules out third-party APIs, we design for that from the start.
Development is included in the project price. Model and API usage is billed at cost, based on your actual volume. We estimate it up front and then work to bring it down - compression, caching and smaller models where a smaller model is enough.
Monitoring, tuning and one support loop that runs from the engineers who built it. AI system drift-data changes, storefronts change, We watch for it and we fix it.