Document Reconciliation Engine

A database tells you what was true.
Elara tells you what's true now.

An AI reconciliation engine for regulated industries. Elara cross-checks every specification, formula and certificate against the live record — and shows you, with evidence, exactly what changed.

First proven in cosmetics with RCMA Makeup. Open now to supplements, food and beverage.

It allowed us to shift from data entry to decision-making.

Agi Denes, Ph.D., MBA, R&D Director
The Research Council of Makeup Artists Inc.
  The film · 2 min
Watch how Elara turns a batch of supplier documents into a decision-ready summary.

The problem

The rules keep changing. The documents keep coming.

Tariff & supply-chain disruption

Supplier landscapes shift monthly. HTS classifications and country-of-origin documents need constant re-verification.

A calendar that keeps moving

MoCRA’s registration and listing duties are in force, and its GMP rule has no date. The EU AI Act is law, with high-risk obligations from December 2027. The EU’s GMP annex on AI is still a draft. For supplements and food the calendar moved too, and the rules already in force do not: a supplier’s certificate counts only from a qualified supplier, with the method, the limits and the actual result on it. The dates move. The question doesn’t: show the record, and show why you believe it.

MoCRA · in force, GMP rule undatedEU AI Act · law, high-risk duties from Dec 2027EU GMP Annex 22 · draftFDA CSA · final21 CFR 111 · in force, CoA reliance conditionalFSMA 204 · no enforcement before Jul 2028

Manual reconciliation breaks at scale

Analyst-driven review cannot keep pace with the volume these forces generate. The bottleneck is no longer regulation or sourcing — it is human capacity to read, interpret, and reconcile.

Until it all lands on one desk. Yours.

How it works

Hand Elara a batch of documents. Get back a report you can act on.

We expected document extraction; instead, Elara reasons across multiple documents, identifies inconsistencies, and answers questions we never thought to include in the requirements.

Agi Denes, Ph.D., MBA, R&D Director
The Research Council of Makeup Artists Inc.
iReads every document
iiInterprets each field against your definitions
iiiReconciles against the live record
ivReturns a decision-ready summary

Generic extraction is the easy part. Each field is read against your own definitions before any two sources are compared: a label with its value, a threshold never taken for absence, silence never turned into a number.

Elara flags what needs attention. People decide. Gaps, conflicts, and compliance-critical items are surfaced — never buried in the output.

Rosehip Seed Oil — Supplier Spec Decision-ready summary
What I know now
INCI: Rosa Canina Fruit OilCAS 84696-47-9Lot RC-2287Cold-pressed
Claims & compliance signals
Origin: ChileFunction: emollientAllergen-freeMoCRA-relevant
Still needs attention
CoA missing for Lot RC-2287Origin conflicts with prior shipment

One way of reading. Three sectors.

Illustrative · synthetic data

Cosmetics

0.02% of a preservative, “carried over from the aloe juice, not added.” The composition doesn’t move. A formaldehyde releaser is present whatever its role, so the free-from on file is flagged for review.

Supplements

“Lead: NMT 0.5 ppm, Complies,” and no other metal, against “Heavy metals: none” on file. A pass is a limit met, not a result found, and one metal doesn’t answer for the rest. The “none” is flagged for review.

Food

“Same natural flavor, new carrier”: maltodextrin (wheat), gluten “below 20 ppm,” against “no wheat” on file. Below 20 ppm meets the gluten-free threshold, but the carrier’s wheat source may still require allergen labeling, so it’s flagged for review as a potential new allergen.

It felt like I was working with a technical analyst.

Agi Denes, Ph.D., MBA, R&D Director
The Research Council of Makeup Artists Inc.

Instead of simply choosing one value, it explained discrepancies, identified which document it considered the more reliable source and why.

Agi Denes, Ph.D., MBA, R&D Director
The Research Council of Makeup Artists Inc.

ProofThe RCMA pilot

A four-week production-readiness pilot with RCMA Makeup — structured as a no-fee proof-of-value engagement to validate the platform in production.

Platform operating cost $13.44
yielded
Analyst time saved $9,800 Implementation and deployment investment separate.

99.3%

Extraction accuracy across 140 documents

2.4 min

Average processing time per document

163 hrs

Analyst time recovered over four weeks

The pilot trial was a validation exercise for us, with acceptance criteria agreed upon before we processed a single document.

Agi Denes, Ph.D., MBA, R&D Director · The Research Council of Makeup Artists Inc.

Read the full case study →

RCMA approved production deployment.
proven with

Why Elara

Built by the people who understand your work.

Most AI vendors are technologists guessing at regulated workflows. Smart-Suited Tech came from those workflows — a process-improvement and regulated-operations background, with the six-sigma DNA to match.

That means Elara was built by people who have lived the actual operational problem it solves: the contradictory documents, the audit questions, the cost of getting reconciliation wrong.

Easy-to-use, intuitive, and clearly designed with the user in mind.

Agi Denes, Ph.D., MBA, R&D Director
The Research Council of Makeup Artists Inc.

When I required all data processing to remain within the US, Tom reworked the system configuration around that constraint on schedule and without friction.

Audra Sanchez, Director of Operations
The Research Council of Makeup Artists Inc.

Your documents are processed in a dedicated enterprise cloud tenant, US data residency configurable. Nothing trains any model. Everything is deleted on a stated schedule — in writing.

a product of Smart-Suited Tech

Ready when you are

The same pilot RCMA Makeup ran is available to you.

Built by the people who understand your work.