Product Status · Internal Review

What we are building, where it stands, and what it taught us.

2026-07-03 · companion to the AI Product Guide

01 · THE PRODUCT

One system, one shape.

A human-in-the-loop pipeline that turns messy inbound customer communication into structured commercial output. Every client is a configuration of this same pipeline, never a rebuild.

1 Gather→ 2 Identify→ 3 Parse→ 4 Match→ 5 Human loop→ 6 Act & deliver

Foundation: LLM gateway, tenancy, trust, cost control, observability

01 · THE PRODUCT

Six areas in a chain, one shared foundation.

01

Gather

Every channel a client's customers use, in one place

02

Identify & tag

What is this: request, clarification, noise?

03

Parse

Items requested, plus the context around them

04

Match

Request × system of record, schema in the middle

05

Human loop

Humans fix cases; their behavior teaches the product

06

Act & deliver

Branded quote, ERP write, delivery

02 · AREA 01

Gather

Shipped

Outlook + Gmail OAuth pollers, 5-min beat

Attachment extraction: PDF, Excel, CSV, ZIP, image OCR

Dedup by fingerprint + provider message-id

Manual .eml upload

Partial / missing

#268Body truncated at 10k chars, no flag

#270Scanned PDFs never reach the vision path

#252 P0Reply has no structural link to its case

not trackedWhatsApp, phone calls

Priority: #268 → #270 → the #252 thread model the moment its workflow questions are answered.

02 · AREA 02

Identify and tag

Shipped

Classifier with confidence gate

Full audit log of every decision

Customer match at intake

Partial / missing

#252Clarification reply tagged INQUIRY, silently discarded

#198No email-direction awareness: supplier mail misread as RFQ

futureCase types beyond RFQ (orders, returns, after-sales)

Worst silent failure in the product: a client asking a question gets no row, no extraction, no signal, just silence.

02 · AREA 03

Parse

Parse output is not a list of items. It is items plus a context ledger. "Outdoor project" constrains every item's IP rating. "Like this SKU but horizontal" is a reference plus a modification, not free text.

Shipped

Line-item extraction + prompt-injection defence

Spec normalizer + extractors (watt, lumen, kelvin, IP, dims, UGR)

Partial / missing

#271One whole-email context only: mixed projects unmatchable

not trackedThe context ledger · relational requests

#271 is the flagship known bug: headers detected, then dropped. It gates the extraction restructure and is where this belief was born.

02 · AREA 04

Match

Parsed request on one side, the client's system of record on the other. The attribute schema is the bridge. This is where the value technology concentrates.

Shipped

Cheap-first chain: code → register → competitor → retrieval → judge

LLM catalog enrichment with human review

Partial / missing

#277Schema still lives in code, not a written artifact

#277/2782 live invariant violations: CRI, UGR judged with no data

Method: attribute-schema-bootstrap-playbook.md. Derive it once, checkably, never through incidents.

02 · AREA 05

Human loop: two loops share the screen

Loop 1: fix this case

Two-panel review UI, quote builder

Per-item override, assignment, locking

Loop 2: teach the product

44-case eval corpus, 3-phase reporter

not trackedBehavioral signal taxonomy · coverage KPI

Loop 2 is the biggest gap. Only the final SKU correction is captured. The coverage KPI (diffing Odoo orders against platform-created ones to catch bypass) is cheap to build and unmeasured today.

02 · AREA 06

Act and deliver

Shipped

Quote PDF · Excel export

Idempotent Odoo draft order, never auto-confirms

Partial / missing

Delivery stub: hardcoded to an internal address

Automated send · fiscal compliance · translated output

02 · FOUNDATION

The trust cluster: pending, urgent

The boring substrate every product shares, built once. Four urgent audit findings sit here; none are optional before a security claim to a client.

Shipped

Async workers, retry, cost tracking per call

JWT auth, error monitoring, prod Docker profile

Missing, urgent

LLM gateway · EU data residency · ZDR contract

Field encryption · secret manager · tenant_id · rate limiting

03 · ARONLIGHT

Where we are

Live in production at aronlight.adaptto.ai, running the full chain end to end on an all-Haiku pipeline with per-call cost accounting.

Now

META #259: matching restructure META #267: extraction restructure Wave 1 (9 fixes, P0) Wave 2 (8 items, P1)

Next, after the baseline

#271 section-aware extraction #261 hybrid retrieval #277/278 schema + CI #227 correction flywheel

Step zero of everything: #262, the deterministic eval baseline. Identical code scored 39.3% vs 49.6% back to back. Until this lands, no matcher change can be measured honestly.

03 · ARONLIGHT

What building it taught us

01

Schema first, never incidents.Four same-class matching bugs in a week were all knowable in week zero.

02

Silent failure is the default state.Truncated emails, blank OCR, discarded replies: none threw an error.

03

You cannot improve what you cannot measure.±10-point eval noise was hiding every real regression.

04

Catalog data quality beats matcher cleverness.1,000+ uncategorized products explain more misses than any ranking bug.

05

Closed does not mean done.Security issues closed without implementation created false confidence.

06

Structure must survive parsing.Flattened headers and columns make downstream matching structurally impossible.

07

The human loop only teaches if it's wired.Corrections were captured, not fed back. Learning is built, not assumed.

04 · TWO GUIDES, ONE WORK LIST

Where the guides disagree is the roadmap.

The System Guide says what good looks like, product-agnostic. The Product Guide says what exists, this product specifically. The gap between them is the work list.

01

Deterministic eval baseline#262: the measurement gate, step zero of Wave 1

02

The trust clusterGateway, ZDR, encryption, secrets, corpus anonymization

03

Wave 1 + Wave 2 execution17 spec'd fixes, delegated, agent-runnable

04

Schema formalization#277/#278: derive it, then enforce its invariants in CI

05

Adaptto Core decision#55: lands once, hard to retrofit

06

File the new beliefs as issuesContext ledger, relational requests, taxonomy, coverage KPI

05 · WHAT MATTERS MOST

Not just "an AI that reads emails."

01

Context is the moat

Items are easy. Understanding that "outdoor" constrains every item, or that a tender implies specs nobody wrote, is the hard and valuable part.

02

The schema is the technology

Between a messy request and a 4,300-product catalog sits a checkable, portable schema. It turns a wrong quote into a build failure.

03

The product learns from use

Every accept, override, and abandonment is a verdict on one part of the chain. Wiring these signals is what compounds.

06 · NEW CLIENTS

What onboarding looks like, area by area

Because every client is a configuration of the same chain, coverage reads per area.

Area	Second lighting client	New vertical
Gather	Strong	Good
Identify	Strong	Good
Parse	Good	Partial
Match	Strong	Partial
Human loop	Strong / Weak	Same
Act	Good	Partial
Foundation	Weak	Weak

The two weak cells are the same for every client. That's exactly why they rank high on the roadmap. Paid once, every client benefits.

Full detail

The registry behind this story

ai-product-guide.md: every claim, file path, and issue number

ai-system-best-practice-guide-reviewed.md: the best-practice lens

attribute-schema-bootstrap-playbook.md: the engagement method