RAG answers questions from a corpus you control and can cite. Fine-tuning shapes model behaviour and small specialised tasks when you own training signal. Agents plan steps and call tools under policies. Most production systems compose two of these. The failure mode is picking the buzzword instead of naming the decision the software must make.
Ships grounded assistants, IDP pipelines, and tool-backed agents for enterprise workflows.
If your users need answers grounded in manuals, policies, tickets, or contracts, RAG is the default in 2026 because freshness and provenance beat memorization. The work is not “vector database in a diagram.” The work is chunking, access control at retrieval time, re-ranking, evaluation sets grounded in real operator questions, and policies for refusal when evidence is weak.
Fine-tuning still earns budget when you have high-quality labelled pairs that teach tone, format, or a narrow classification boundary, and when those patterns do not change weekly. It is weaker as a substitute for a living document corpus. The model cannot cite what it memorised, and updates require retraining discipline.
Agents matter when the system must update tickets, issue refunds within policy, query internal APIs, or orchestrate multi-step workflows with retries and compensating steps. Agents are not “RAG plus confidence.” They are policy-bound tool use with explicit failure handling. If your workflow is mostly read-only Q&A, an agent buys complexity you do not need.
The winning architectures we see in 2026 are boring on purpose: explicit states, explicit tool permissions, explicit eval gates. The flashy demo is a single graph node labeled “AI” with arrows to sixteen systems. Production is smaller graphs with logs.
Ask: what is the user decision at the end? Approve, deny, post, route, or only understand? If it is only understanding, start with RAG. If it is classification with stable labels, consider fine-tuning. If it is action under constraints, design the agent policy matrix first, then decide whether retrieval is even in scope.
If you are stuck in a debate, run a Rapid POC that implements two architectures on the same acceptance tests. The spreadsheet beats the whiteboard. We routinely recommend the simpler architecture when it clears the bar, because simpler is what your team will operate at 2 a.m. on a Sunday.
Same-topic posts first, then adjacent practices.
A Rapid POC is a sandboxed working build on your real systems and a bounded slice of your real data, designed to answer procurement questions that documents cannot. An RFP still has a role when compliance requires apples-to-apples comparisons, but it is a poor primary tool for AI because the risk is behavioural (models under your traffic, on your documents) and not a feature matrix.
Read the articleUnstructured data is any payload where meaning is not already in neat rows. Email bodies, PDF contracts, call recordings, images from the field, and the long tail of notes fields your teams misuse because your structured schema never matched reality. If you only warehouse structured tables, you are flying half blind on what actually happened in operations.
Read the articleIntelligent document processing (IDP) is the discipline of turning documents into decisions. Classify, extract, validate, route, and post, with measurable straight-through processing. In 2026, layout-aware vision-language models raised accuracy ceilings on ugly PDFs, but the hard parts remain validation, drift, and the economics of human review.
Read the articleHonest, specific answers tied to the thesis above. Not generic FAQ filler. If something isn't covered here,ask us directly.
Often no. The best system is the one that clears your accuracy, latency, and residency constraints at sustainable cost. We benchmark before we brand.
Agents still need retrieval for grounded answers unless you want them to memorise your corpus, which is both brittle and hard to audit.
Evaluation and monitoring: building the harness that catches regressions when documents, models, or prompts change.
A two-track Rapid POC on identical tests, one RAG-first and one agent-first, scoped narrow enough to finish without heroics.
A short note is enough. We will reply within one business day with a Rapid POC scoping call.
