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.
Ships production AI systems and Rapid POCs for mid-market and enterprise teams.
RFPs assume you can specify the unknown. In AI procurement, the unknown is usually how accuracy, latency, and failure modes look on your corpus, inside your VPC, with your identity model and your exception queues. A vendor can look brilliant on sanitized benchmarks and still collapse when your PDFs include scanned faxes, rotated pages, and handwritten margin notes.
RFPs also incentivize narrative maximalism. Respondents optimize for breadth: every checkbox ticked, every buzzword included. That selection pressure rewards sales engineering, not integration discipline. You discover the gap months later, during implementation, when the real costs show up as change orders and internal rework.
A Rapid POC is a fixed-scope, time-boxed build (an app shell, a backend connected to your real systems, and a small operator surface) that produces measurable outputs on your data so stakeholders can decide with evidence. It is not a hackathon science project. It is scoped to a decision.
RFPs remain useful when you must compare vendors on standardised requirements that do not depend on your private data, for example hardware appliances, licensed software with fixed modules, or services with mature SLAs. They also help when legal requires a documented competitive process. The mistake is treating an RFP as a substitute for measurement on your workload.
We bias POC scopes toward claims that can be proven false quickly. If a vendor says they can extract remittance fields with high precision, the POC should publish a labelled evaluation set from your environment and show precision and recall by field. If they claim sub-second latency at p95, the POC should show traces under concurrent load. If they cannot meet the claim in two weeks, that is data, not drama.
Use redaction or synthetic analogs where needed, isolate keys and data paths, and keep production systems read-only until policies approve writes. Document what is still unknown after the POC (long-tail document variants, edge cases in approvals, rare languages) so production planning budgets real engineering time instead of pretending the POC solved the universe.
A good POC ends with three artifacts. A working demo environment stakeholders can click, a metrics table tied to acceptance tests, and a production plan with explicit hardening milestones (monitoring, drift detection, human review queues, and rollback). A bad POC ends with a slide that says we are very confident.
We scope 14 days by default, align on acceptance tests up front, and ship weekly increments inside the sandbox. You get a clear recommendation: expand, pivot, or stop. If you want procurement to remain involved, we can structure the POC outputs as attachments to your RFP decision memo. Evidence inside the process, not instead of governance.
If you are weighing a long RFP against a short POC, ask one question. What would change your mind faster, another paragraph about AI excellence, or a CSV of extracted fields from your own PDFs with confidence scores attached?
Same-topic posts first, then adjacent practices.
Unstructured 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 articleRAG 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.
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.
No. It informs legal and security review with concrete artifacts: data flows, logs, and measured behavior. Your counsel still decides what contracts require.
Yes, sequentially or in parallel on disjoint scopes if your team can absorb the coordination cost. Parallel POCs are expensive; they should be reserved for short lists after basic diligence.
Then the POC should narrow further (one document family, one queue, one integration) until the scope is honest. A POC that promises everything proves nothing.
Shared acceptance tests, identical evaluation slices, and blinded scoring where possible. The metric sheet is the comparison, not the prose.
A short note is enough. We will reply within one business day with a Rapid POC scoping call.
