Whitepaper
Architecture of a budget-governed, brandable AI intake system
Public AI chat widgets fail their operators in two directions at once. Left open, they burn API spend on whatever visitors type and say things the business never approved. Locked down, they become decoration: a search box that apologizes. Most teams ship one, discover both failure modes, and quietly remove it.
The terminal at workflowtech.ai is our answer, and this paper describes how it works. The same architecture runs as GestaltWorkframe, the reusable framework the terminal is built on.
The terminal never starts as a blank prompt. A short structured intake (what are you hoping to accomplish, what are you building, how automated are you today, what would help right now) routes every session into one of three persona modes (service inquiry, practitioner assistance, education) before the first model call happens. The router, not the model, decides mode transitions, tool access, and escalation. Models are workers; the backend owns orchestration.
Presentation matches the metaphor: intake and responses run as a live prompt session rather than a card UI, and the whole session is available in English and in es-CO Spanish.
Every turn resolves through a provider router with an ordered route table:
Spend governance is only as honest as its accounting. Every successful cloud completion must report token usage the ledger can read; a completion that cannot be metered trips a persistent accounting breaker that takes every cloud route offline until an operator clears it. We have watched this breaker fire in production on a healthy response with an unreadable usage format. That is the design working: unmetered spend is treated as an outage, not a rounding error.
The public status indicator reports the same truth. “Online” is computed from whether a route can actually serve the next turn, not from whether keys are configured.
Answers are grounded in a curated corpus, and the ranking that selects the grounding is the part most systems leave to intuition. Ours runs in three stages: an on-disk FTS5 lexical index and a dense vector index each produce candidates, the two lists are fused, and a cross-encoder reranks the head of the fused order, scoring query and passage together, which the two retrievers structurally cannot do.
Each stage had to earn its place against a 73-question golden set built from real questions, every one paired with the specific production chunk that should answer it, evaluated over the live 63,105-chunk corpus:
| Configuration | recall@3 | MRR |
|---|---|---|
| Fused candidates, no rerank | 0.4247 | 0.3823 |
| With cross-encoder rerank | 0.5068 | 0.4120 |
The same harness removed code. Two hand-written query classifiers adjusted scores by substring match on the source path and the chunk text. Measured, one fired on 0 of 73 questions and the other on 7, six of which were false positives; on the aggregate they were a wash to four decimal places. They were deleted rather than defended. The golden set and the eval script now live in the repository next to the code they measure, and the harness asserts its own ordering against real search output so it cannot silently drift from the path it claims to be scoring.
Deployments are configuration, not forks. A deployment bundle (brand palette, logo, identity, intake questions, personas, routing policy, copy) is a directory of YAML the framework loads at runtime. The chaos entry animation on workflowtech.ai (the ant mark that assembles from particles and converges into the terminal frame) is a framework capability any deployment can switch on with its own logo; the particle engine samples whatever mark the bundle provides.
The same system curates its own public material. A discovery watchlist (RSS, GitHub repositories and topics, subreddits, YouTube channels, web diffs) feeds scheduled polling; a scout scores finds; notable items flow to a public ticker, a curated every-ten-days digest, and a Git-backed public library (CODEX) that is both human-browseable and machine-readable. Featuring, curation, and newsletter queueing are explicit operator actions with audit records.
Live terminal: workflowtech.ai/terminal · Public library: workflowtech.ai/codex
Retrieval eval: the golden set and the harness that produced the numbers above are in the repository at kb/eval/.
Credentials, all Anthropic-issued and verifiable on Credly: Claude Partner Badge: Claude Code · Claude Certified Architect: Professional (CCAR-P) · Architect: Foundations (CCAR-F) · Developer: Foundations (CCDV-F).
Contact: [email protected]