i-360 services
RAG Development Services
RAG development services connect a language model to approved organizational information. I-360 designs retrieval augmented generation services around source quality, access control, evidence, evaluation, and the application workflow in which an answer will be used.
- Sources
- Ingest
- Preprocess
- Chunk and tag
- Embed and index
- Retrieve
- Construct prompt
- Generate
- Show evidence
From sources to a grounded answer
A RAG pipeline ingests approved sources, preprocesses their content, divides it into useful chunks, records metadata, and creates embeddings where semantic retrieval is appropriate. A vector database or another suitable vector store supports similarity search; keyword or hybrid retrieval can complement it.
At question time, the application identifies the relevant knowledge collection and permitted sources, retrieves evidence, constructs the model prompt, and generates an answer. Source references let the user inspect the supporting material. Grounded AI answers depend on the evidence available; the system should acknowledge an unsupported question rather than invent a fact.
Sources and ingestion scope
Enterprise RAG solutions can be scoped for PDFs, DOCX, TXT, Markdown, websites, sitemaps, JSON, CSV, and XLSX. Policies, procedures, internal documentation, product documentation, and company knowledge often need different preparation and update rules.
Ingestion handles extraction quality, source identity, duplicate material, access metadata, and failed imports. Tables and structured records may need a different representation from prose. Scanned files require an explicitly selected OCR path. We assess the actual sources before promising coverage.
Chunking, metadata and retrieval design
Chunking should retain enough context to answer a question without mixing unrelated material. Metadata can identify the source, section, version, owner, knowledge base, and access boundary. Embeddings support semantic similarity, but retrieval quality still depends on the content and the question.
Hybrid retrieval combines signals when useful; keyword fallback can help with exact names and identifiers. Retrieval evaluation checks whether relevant evidence was found before assessing the generated answer. A fluent response cannot compensate for retrieving the wrong policy version.
Knowledge governance and document lifecycle
AI knowledge management needs an owner for source approval, review, updates, activation, and retirement. A draft collection can be tested before it becomes available to users. Versioning and source integrity help teams understand what changed and what an answer was based on.
Duplicate and ambiguous content should be reviewed rather than silently accumulated. Superseded documents need a defined retirement process. Evaluation questions should include conflicting versions, missing information, and requests beyond the approved knowledge scope.
Access control and private deployment
Private RAG solutions may run on customer-controlled infrastructure with local Ollama models, or use commercial APIs under an agreed data policy. Private enterprise AI requires more than local inference: ingestion, storage, backups, logs, administrative access, and any external requests must also be controlled.
Access filtering should apply before evidence reaches the model. Separate knowledge bases and permissions can support departments or organizations with different information boundaries. The architecture defines whether conversation history is retained and who may inspect it.
Evaluation, hallucination controls and refusal
A RAG knowledge assistant is evaluated on evidence relevance, source references, supported conclusions, appropriate uncertainty, and behavior when the answer is absent. The test set should represent real user questions, exact lookups, ambiguous requests, and access-sensitive cases.
Hallucination controls combine better retrieval, constrained prompts, output validation, and unsupported-claim refusal. They reduce risk but do not guarantee perfect answers. Human escalation and source inspection remain useful for consequential decisions.
Wisdom 360 as a product foundation
Wisdom 360 is the documented enterprise knowledge assistant in the i-360 portfolio. Its published capabilities include multiple knowledge bases, supported-source ingestion, draft review, semantic retrieval, keyword fallback, local Ollama integration, source evidence, and knowledge administration.
A product deployment and a custom RAG implementation are different engagements. We first compare the documented product with your required sources, permissions, interface, and integrations. Private company chatbot delivery offers a focused starting scope; this page covers the broader retrieval architecture and engineering service.
Deliverables and implementation path
RAG implementation services can deliver a source inventory, ingestion pipeline, retrieval configuration, application integration, evaluation set, review workflow, deployment configuration, and operating instructions. The selected components and supported source types are stated in the proposal.
Bring sample sources, expected questions, access rules, update frequency, infrastructure constraints, and the people responsible for source quality. Enterprise AI consulting can establish readiness, while AI implementation covers production integration and rollout.
Questions before you start
Does RAG eliminate hallucinations?
No. Retrieval and output controls can reduce unsupported answers, but source quality, retrieval quality, evaluation and escalation remain necessary.
Can each department have its own knowledge?
The architecture can define separate collections and access boundaries. These must be enforced before evidence is supplied to the model.
Can we use local models?
Local Ollama integration can be assessed against hardware, response quality, context requirements and operating responsibilities.
What should we provide for a pilot?
Provide approved sample sources, representative questions, access rules, required source references and the criteria reviewers will use to judge an answer.
Related services and product examples
Define a useful next step.
Share the current workflow, systems, constraints, and result you want to review. We will discuss an appropriate scope and the inputs it needs.