Entific Immigration, now live

We make firms AI-native.

Where mistakes in the paperwork have real consequences. We take the work off your desk & embed automated domain intelligence at every step.

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Before Entific, our team built for

  • Nasdaq
  • Broadridge
  • Microsoft
  • Samsung
  • SAP
  • Lockheed Martin
  • UBS
  • BMO
  • Barclays
  • IBM

Now live

Entific Immigration

Audit an application before it's filed, research across every Federal Court decision, and build work product grounded in your client's record — every claim pinned to a source you can open.

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Entific Workflow · Dossier TRV refusal · judicial review

On the file

Built to verify, not just generate.

Most AI tools draft, summarize, or predict. Entific reads everything in the file, checks it against your firm's own records and the rules that govern your work, and pins every finding to a source you can open.

Reads everything

No sampling, no skimming. Every page of every document, every time.

OCR built for the messy real world, not clean digital text — scanned pages, handwritten fields, low-quality multi-page PDFs. Extraction pulls names, dates, amounts, and clauses into structured, comparable fields.

Passport extraction
Passport scan.pdf p. 1
CANADA Passport
Surname CHEN
Given names Wei
Date of birth 14 Mar 1987
Passport no. AB1234567
  • Surname CHEN
  • Given names Wei
  • Date of birth 14 Mar 1987
  • Passport no. AB1234567

Grounded in your records

Every claim is checked against the client's actual file, not a generic model's best guess.

The date of birth read from the passport scan is cross-referenced against every other document in the case — the application form, the birth certificate, governing rules — flagging where two sources disagree.

Record cross-check
From passport extraction Date of birth 14 Mar 1987 Passport scan.pdf
Conflict 3 of 12

Date of birth

IMM 0008 · Personal details

The form says

Application.pdf · p. 3 14 Mar 1989

The record says

Passport scan.pdf · p. 1 14 Mar 1987
Birth certificate.pdf · p. 1 14 Mar 1987

Pinned to a source

Every finding cites exactly where it came from, so it can be verified in one click.

Open the passport scan from that conflict and the date of birth is highlighted on the page it was read from — not paraphrased, not summarized, the exact field in the original PDF.

Source viewer
From record cross-check Date of birth · conflict

The record says

14 Mar 1987

Passport scan.pdf · p. 1
Passport scan.pdf p. 1
CANADA Passport
Surname CHEN
Given names Wei
Date of birth 14 Mar 1987

Date of birth · read from scan

Applies domain knowledge

Every check runs against the specific rules, procedures, or precedent that actually govern the work — not just internal consistency.

The engine reads whatever corpus defines correctness in that field. For a mortgage file, that's the lender's underwriting guidelines. For a prior authorization request, a payer's medical necessity criteria. Swap the corpus, and the same engine audits an entirely different practice.

Corpus audit

Mortgage file

Lender underwriting guidelines

  • Debt-to-income ratio ≤ 43%
  • Appraisal age ≤ 120 days
  • Income verification 2 years W-2

Prior authorization

Payer medical necessity criteria

  • Diagnosis code Supports indication
  • Prior conservative therapy Documented failure
  • Clinical notes Within 90 days
Loan application.pdf PA request form.pdf

DTI 47% — exceeds underwriting guideline

No documented prior conservative therapy

Closes the loop with real output

The work isn't done at "here's what's wrong." It ends in the actual document the case needs — a memo, a completed form, a ready-to-submit filing.

Every finding feeds directly into the deliverable the work actually requires. A mortgage file that clears underwriting doesn't stop at a checklist — it becomes the completed application, submitted in the lender's own format. The output is the work product, not a summary of it.

Work product
  • DTI 41% — within guideline
  • Appraisal 45 days — current
  • Income verified · 2 years W-2
  • Diagnosis supports indication
  • Prior therapy documented
  • Clinical notes within 90 days
Uniform Residential Loan Application Ready to submit
Borrower
Jamie Rivera
Loan amount
$412,000
Debt-to-income
41%
Property address
1842 Oak Lane, Portland OR

Lender format · exported

Prior authorization request Ready to submit
Patient
M. Okonkwo
Requested service
Lumbar MRI · 72148
Diagnosis
M54.5 · Low back pain
Clinical rationale
6 wks conservative therapy failed

Payer portal format · exported

Entific is for firms where rules matter.

Three colleagues on an office landing, looking over papers — glass offices beyond

We help teams doing regulated, document-intensive work — where people have to read, verify, interpret, and produce filings with no room for mistakes.

We automate this work, with domain intelligence at every turn.

  1. Automate the work that keeps the firm moving.

    Turn document-heavy administrative processes into workflows that run automatically — from intake and extraction through processing and completion.

    • Document intake
    • Data extraction
    • Reconciliation
    • Form completion
    • Claims processing
    • Insurance coordination

And design it entirely around your firm.

Three colleagues on an office landing, looking over papers — glass offices beyond

Configure for your firm

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Our team

  • Salman Alam

    Salman Alam

    Co-founder

  • Kelso Curtis

    Kelso Curtis

    Co-founder

  • Eiman Sadegh

    Eiman Sadegh

    Advisor

We've worked & built for high-stakes domains.

We've built software inside banks, exchanges, and regulated infrastructure — and practiced directly in regulated fields ourselves. We bring the discipline of high-stakes engineering, and the judgment of people who've actually done the work.

Grounded in your records.
Running on your rules.

Turn your rules, knowledge, and processes into AI-powered workflows built around how your firm works.

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A research lab configuring engines for high-stakes firms.

© 2026 Entific Labs