Şüheda Yıldırım
FOUNDER & CEO
Data & AI platform engineer turning messy enterprise data into structured, reliable information.
LinkedInTurn messy files into clean, trusted data, automatically.
Ingest AI ingests raw spreadsheets and PDFs and outputs validated, structured data, 100% accuracy on critical fields, zero manual cleanup, every transformation traceable.
Zero hallucination. Zero data loss.
Two Ways to Work With Us
Choose the model that fits how your data and systems operate today.
Self-Service
Upload & Convert
For teams that prefer to keep files local and don’t require system integration.
- Upload spreadsheets, PDFs, or exports manually
- Download validated, standardized output
- All exceptions clearly flagged — nothing invented
- No access to your internal systems required
Enterprise Integration
System-to-System Integration
For organizations that need continuous, automated data processing inside their workflow.
- Direct integration with PIM, ERP, or internal tools
- Automated ingestion and structured output
- Schema enforcement and validation at scale
- Full auditability and long-term reliability
Who we are
The team behind Ingest AI.
AL
Aaron Lindner, PhD
STRATEGIC ADVISOR
Strategic advisor supporting positioning, partnerships, and pragmatic execution for early-stage growth.
LinkedInWhy Ingest AI
Not another AI tool.
A data guarantee.
Most tools "help" with data. We deliver production-ready, validated output with zero hallucination, zero data loss, every time.
Other tools
General-Purpose AI Assistants
They can chat about your data, maybe write a script. But they guess, hallucinate, and leave you to verify everything manually.
- ✕ Hallucinate values that look plausible but aren't real
- ✕ Silently drop rows when context windows overflow
- ✕ No validation pipeline , you're the QA team
- ✕ Different output every time you run the same file
Ingest AI
Purpose-Built Data Engine
We don't chat about your data, we transform it. Deterministic, validated, complete. Every row accounted for, every value verified.
- ✓ Zero hallucination: every output traces to source
- ✓ Zero data loss: row-level completeness checks
- ✓ Built-in validation before delivery
- ✓ Consistent, reproducible results at any scale
Other tools
Enterprise Cloud Extractors
Powerful: if you have a cloud engineering team, months to deploy, and budget for custom model training. Built for tech companies, not ops teams.
- ✕ Require cloud infrastructure setup & maintenance
- ✕ Weeks of model training per document type
- ✕ Need developers to build integration pipelines
- ✕ Per-page pricing adds up fast at scale
Ingest AI
Zero-Setup Intelligence
Upload your messy file. Get clean, structured data back. No infrastructure. No training. No engineering team required.
- ✓ Works in minutes, not months
- ✓ Self-service or enterprise API: your choice
- ✓ Handles PDFs, spreadsheets, and mixed formats
- ✓ Predictable pricing, no surprise bills
Other tools
Rule-Based Cleaning Platforms
Great at deduplication and formatting: if your data fits their rigid templates. Falls apart the moment files get messy, inconsistent, or unstructured.
- ✕ Can't handle unstructured or semi-structured data
- ✕ Brittle rules break on format variations
- ✕ Manual configuration for every new source
- ✕ No intelligence: just pattern matching
Ingest AI
Adaptive Standardization
Understands messy, real-world data: variant formats, inconsistent headers, mixed structures. Adapts to the chaos, delivers the order.
- ✓ Intelligent parsing across any file format
- ✓ Learns structure from context, not rigid rules
- ✓ One tool for PDFs, Excel, CSVs, and more
- ✓ AI-powered with human-grade accuracy
Other tools
Spreadsheet AI Add-ons
Handy for formula help and basic cleanup. But they live inside your spreadsheet, limited to one file at a time, no cross-format understanding.
- ✕ Confined to a single spreadsheet context
- ✕ Can't ingest PDFs or non-tabular sources
- ✕ No batch processing or pipeline capabilities
- ✕ Fixes symptoms, not the data pipeline
Ingest AI
End-to-End Data Pipeline
From raw, messy source files to clean, validated, integration-ready data. Not a feature inside another tool, a dedicated pipeline that replaces the manual chaos.
- ✓ Batch process hundreds of files at once
- ✓ Cross-format: PDFs + spreadsheets in one run
- ✓ API-ready output for direct system integration
- ✓ Replaces manual work, not just assists it
Send us your messiest file.
We'll send back clean data.
No signup required. See real results on your actual data.
How it works
Three steps to reliable, validated data.
1
📤
Import files
Spreadsheets, PDFs, CSVs — in whatever format partners send.
Common issues
- Column drift & inconsistent headers
- Mixed units and currencies
- Missing fields & messy rows
2
⚙️
Standardize + validate
Map to your schema, normalize values, and run checks.
Pipeline
- Schema-aware mapping
- Unit/currency normalization
- Value cleanup (dates/IDs)
- Business rule validation
Anti-hallucination:
If data isn’t in the file, Ingest AI doesn’t invent — it flags it.
3
✅
Deliver output
System-ready dataset + exceptions + change summary.
Deliverables
- Clean schema + consistent naming
- Validated critical fields
- Explicit exception list
- Traceable transformation summary
🛡️ Data integrity (no hallucinations)
AI is used to interpret structure — never to invent values. Every output value is either traceable to the input or explicitly flagged.
Zero fabrication: missing/ambiguous fields are flagged, not filled.
Protected critical fields: price/amount/IDs get extra validation.
Exceptions are explicit: conflicts are surfaced, never hidden.
Traceable changes: what was mapped/normalized is summarized.
How we handle your data
Your documents stay yours.
Always.
We understand that enterprise documents contain sensitive information. Here is exactly how we treat your data — before, during, and after processing.
Permanent deletion after delivery
Every document you share is permanently deleted immediately after the structured output is delivered. Nothing is retained on our side.
Never used for training
Your data is never used to train, fine-tune, or improve any model. Your documents are inputs to a pipeline — not training material.
NDA before first file
We sign a mutual Non-Disclosure Agreement before any documents are exchanged. This is our default, not something you have to ask for.
GDPR-aligned DPA
We operate under a Data Processing Agreement aligned with GDPR requirements. Available for your legal team to review before any engagement begins.
Full audit trail
Every output value is traceable to its source in the input document. Nothing is inferred or invented — which means every transformation is explainable.
EU-based entity & infrastructure
Ingest AI is a registered German company (UG). All processing happens within EU infrastructure. No data leaves the EU.
Our data handling protocol
What happens to a document from the moment it's received to the moment it's deleted.
1
NDA signed before transfer
Before any file is shared, both parties sign a mutual NDA. No document moves until this is in place.
2
Secure, isolated processing
Documents are processed in an isolated pipeline environment. No document is accessible outside the active processing job.
3
Output delivered with full trace
You receive the structured output alongside an exceptions log. Every value is traceable to the source document. Nothing is invented.
4
Immediate and permanent deletion
Immediately after delivery, all input documents are permanently deleted from our systems. No copies, no backups, no retention.
What we commit to, in writing
These are not policies buried in a terms page. They are contractual commitments.
✓
Mutual NDA before any file is exchangedStandard on every engagement. Not optional, not on request — default.
✓
GDPR-compliant Data Processing AgreementAvailable to your legal team before engagement begins. We will adapt it to your requirements.
✓
No data retained after output deliveryPermanent deletion is part of our standard operating procedure, not an optional setting.
✓
No model training on customer data — everYour documents are never used to train, evaluate, or improve any model.
✓
Processing stays within EU infrastructureNo data is transferred outside the European Union at any point in the pipeline.
🇪🇺
Infrastructure
Built and registered in Germany.
Processing stays in the EU.
Processing stays in the EU.
Ingest AI UG is a registered German company based in Berlin. All document processing happens within EU-based infrastructure. For DACH companies with data residency requirements, this is not a workaround — it is the default.
Questions about data handling?
If your legal or compliance team has specific requirements, we are happy to discuss them directly before any files are shared.
FAQ
Is our data safe with you? What happens to our documents?
Documents are processed under a strict protocol and permanently deleted immediately after output is delivered. Nothing is stored after the job is done. No files are retained, no data is used for model training, and no information is shared with third parties.
All processing runs on EU infrastructure. We sign an NDA before any documents are exchanged. A Data Processing Agreement (DPA) aligned with GDPR requirements is included before engagement begins — we adapt it to your legal team's specifications.
EU infrastructure · Permanent deletion · NDA + DPA includedAre you GDPR compliant?
Yes. Ingest AI is a German-registered company (Berlin) operating entirely on EU infrastructure. Data handling is designed to be GDPR-aligned by default, not as an afterthought.
Concretely: data is processed only for the purpose you send it, deleted post-delivery, never leaves the EU, and never touches a model that trains on client data. A full Data Processing Agreement is included before you send us a single file.
German entity · GDPR-aligned DPA included · No cross-border data transferHow does this integrate with our ERP or existing systems?
The output is delivered as clean, structured data — JSON, CSV, Excel, or whatever format your system expects. You don't need to change anything on your end to receive it.
If you need a direct API integration (e.g., pushing structured output into SAP, your OMS, or a custom ERP), that's part of the scoped project. The pipeline is built to connect to your system, not the other way around. We've handled integrations across different ERP environments and document schemas — and we scope the integration honestly before you commit to anything.
JSON · CSV · Excel · API integration availableHow long does it take to go live?
For a free sample conversion — send us a batch of your documents, get structured output back — that happens within a few days, no commitment required.
For a full production pipeline with API integration into your system, the timeline depends on document complexity and the integration scope. Most projects go live within 4 to 12 weeks. We scope this explicitly at the start and don't move to production until you've validated the output on your own data.
Free sample in days · Full pipeline: 4–12 weeksWe have an internal data team. Can't we just build this ourselves?
You can — but it takes longer than expected and costs more than it looks. The part that usually gets underestimated is not the extraction itself, but the validation layer: what do you do when a supplier sends a format you've never seen? What catches data that gets silently dropped at the intake stage?
Ingest AI's core is exactly that auditing layer — built specifically to handle document chaos at scale, across inconsistent formats, suppliers, and languages. Your data team's time is likely better spent on analysis and decisions, not maintaining parsing rules for every new supplier format that arrives.
We're also happy to work alongside your internal team rather than replace them.
Built-in validation · No silent data loss · Audit trail on every extractionHow do I know the output is actually accurate? What about AI hallucinations?
This is the question we take most seriously. The pipeline is built on rule-heavy, constrained extraction — not open-ended prompting. Every extraction passes through defined validation rules before output is delivered. If something doesn't meet the validation threshold, it's flagged, not silently passed through.
Zero hallucination is an architectural property, not a marketing claim. The pipeline doesn't invent data — it extracts what's there, validates it against rules, and returns it. What it can't extract with confidence, it tells you.
You can also verify this yourself: send us a batch of your real documents and check the output against the source. Most clients do this before signing anything.
Constrained extraction · Validation rules · Zero hallucination by designWhat document types and formats do you support?
Any unstructured document that contains data you need in structured form. In practice this includes: PDFs, invoices, supplier catalogs, spreadsheets, freight documents, financial reports, KYC files, lease documents, policy documents, and mixed-format batches where every document looks different.
If your document type isn't listed here, the right move is to send us a sample. We'll tell you honestly within a day whether the pipeline can handle it and at what accuracy level — before any commitment.
PDF · Excel · Mixed formats · Multi-language · Multi-schemaWhat does it cost?
Pricing is scoped per project based on document volume, complexity, and whether API integration is included. There's no fixed public price because a 50-document batch and a 10,000-document recurring pipeline are fundamentally different jobs.
What we can say: pricing is grounded in the cost of your current manual process. The benchmark question is always what you're spending now on human hours and error corrections — and whether Ingest AI replaces that cost at a fraction of the price.
The fastest way to get a real number is to send us a sample of your documents. We run a free conversion, you validate the output, and we quote based on actual scope — not assumptions.
Volume-based · Scoped per project · Free sample firstWe can't share our actual documents — they're confidential.
That's a common situation and it doesn't block us from moving forward. We sign an NDA before anything is exchanged — you can have it reviewed and signed before a single file is sent. The DPA that comes with every engagement also covers this explicitly.
If your legal or compliance team needs to review our data handling protocol first, we provide that documentation upfront. Some clients also prefer to start with anonymised or synthetic files to validate the pipeline logic before committing real data — we're comfortable with that too.
NDA before first file · DPA included · Synthetic data testing availableHow do we know it'll work on our specific documents before we commit?
You don't have to take our word for it. The standard path is: you send us a real batch of your documents, we run them through the pipeline, and you get the structured output back — before any commercial commitment. You can compare the output against your source files line by line.
We target 95% accuracy on the proof of concept pass. The remaining edge cases are addressed during the full project build, where we investigate every exception and add the validation layers it requires. Final delivery targets 100% accuracy on critical fields and 99% overall, with zero hallucination and any exceptions explicitly flagged rather than silently passed through. The proof of concept gives you enough signal to decide — without committing first.
Free sample run · No commitment required · Full accuracy report includedSee it in action
Select a document type and see it structured.
MFGManufacturing— PDF supplier catalog▾
Raw inputPDF extraction · text artifacts
WEBER BEFESTIGUNGSTECHNIKLIEFERANTEN-KATALOG Rev.12/2025
| ART.NR | BEZEICHNUNG | GEW.g | EP(EUR) | LT |
| M-0042 | Sechskantschr.ISO4017 A2 | 3.2 | 0.08EUR | 3-5Wt |
| M-0043 | Sechskantschr ISO4017 A4 | – | 0.14 € | 3-5Wt |
| M-0044 | MutterSechskantDIN934 A2 | 1.8 | € 0,04 | 1-2Wt |
| M-0045 | Unterlegsch.DIN125 Stahl verz. | 0.7 | 0,02€ | lgrd. |
| M-0047 | Gewindestift DIN913 45H | – | 0.06EUR | 8-10W |
Structure this document
Supplier Parts Catalog5 records structured
OCR artifacts removedMerged cells resolvedColumns to snake_case
Structured outputvalidated
| article_no | description | weight_g | unit_price_eur | lead_time | flag |
|---|---|---|---|---|---|
| M-0042 | Hexagon Screw ISO4017 | 3.2 | 0.08 | 3-5 days | |
| M-0043 | Hexagon Screw ISO4017 | null | 0.14 | 3-5 days | |
| M-0044 | Hexagon Nut DIN934 | 1.8 | 0.04 | 1-2 days | description inferred from merged text |
| M-0045 | Washer DIN125 | 0.7 | 0.02 | in stock |
LOGLogistics— Freight email▾
Raw inputemail body · unstructured
From: [email protected] Subject: Shipments batch 03/2026 AWB LH-990183 | Müller & Co. FRA→CDG 14.5 KG | Maschinenteile | DAP | ETA 15.03.2026 AWB LH-990184 (Schmidt Elektronik HAM to CDG) 2.3 kg, Platinen, EXW, arr. 16/03/26 LH-990186 BioMed BER→PAR 0.8KG Medikamente DDP eta:15/03/26 LH-990188 GlobalChem DUS→MRS 310KG Chemikalien ADR!! CIF 20.03.2026
Structure this document
Freight Manifest4 records structured
Email parsedWeights normalizedDates to YYYY-MM-DD
Structured outputvalidated
| awb | sender | weight_kg | goods | eta_date | flag |
|---|---|---|---|---|---|
| LH-990183 | Müller & Co. | 14.5 | Machine parts | 2026-03-15 | |
| LH-990184 | Schmidt Elektronik | 2.3 | Circuit boards | 2026-03-16 | arrival vs. ETA unclear |
| LH-990186 | BioMed GmbH | 0.8 | Pharmaceuticals | 2026-03-15 | |
| LH-990188 | GlobalChem KG | 310.0 | Chemicals (ADR) | 2026-03-20 | ADR hazmat — compliance required |
WHOWholesale— Excel / CSV▾
Raw inputExcel export · inconsistent formatting
| Art.-Nr. | Bezeichnung | Menge | ME | EP EUR | MwSt. |
|---|---|---|---|---|---|
| KAF-001 | Gastro-Kaffeemaschine XL | 5 | Stk. | 289.00 | 19% |
| KAF-002 | Kaffeemühle M-80 | 3 | Stück | 189 | 19% |
| REI-004 | Reinigungstabs 100er | — | Packung | 12.90 | 7% |
| KAN-010 | Kaffeebecher 300ml | 48 | Stück | 2.80 | 19% |
Structure this document
Purchase Order4 records structured
Units expanded (Stk.→piece)VAT to decimalNames translatedNulls flagged
Structured outputvalidated
| article_no | description | qty | unit | unit_price_eur | vat_rate | flag |
|---|---|---|---|---|---|---|
| KAF-001 | Commercial Coffee Machine XL | 5 | piece | 289.00 | 0.19 | |
| KAF-002 | Commercial Coffee Grinder M-80 | 3 | piece | 189.00 | 0.19 | currency not specified in source |
| REI-004 | Cleaning Tablets 100-pack | null | pack | 12.90 | 0.07 |
FINBanking— PDF invoice + Excel batch▾
Raw input2 sources · PDF + Excel
Source 1 — PDF invoice
RECHNUNG
Nr. RE-20260001
Sigma Trade GmbH
Musterstr. 12, 10115 Berlin
Datum: 01.01.2026Fällig: 31.01.2026Ref: PO-2025-8812Status: offen
Gesamtbetrag nettoEUR 12.450,00
Source 2 — Excel batch
| Rech-Nr. | Datum | Debitor | Betrag | Status |
|---|---|---|---|---|
| RE-20260002 | 03.01.26 | Alpha Logistik AG | 3200.50 | bezahlt |
| RE-20260003 | 05/01/26 | Beta Solutions KG | 7800.00 | überfällig |
| RE-20260004 | 08.01.2026 | Gamma GmbH | 1950.00 | offen |
Structure this document
Accounts Receivable Batch4 records structured
PDF + Excel mergedDates to YYYY-MM-DDStatus translatedDecimal separator fixed
Structured outputvalidated
| invoice_no | invoice_date | debtor | amount_eur | status | flag |
|---|---|---|---|---|---|
| RE-20260001 | 2026-01-01 | Sigma Trade GmbH | 12450.00 | open | |
| RE-20260002 | 2026-01-03 | Alpha Logistik AG | 3200.50 | paid | |
| RE-20260003 | 2026-01-05 | Beta Solutions KG | 7800.00 | overdue | date format ambiguous — DD/MM assumed |
| RE-20260004 | 2026-01-08 | Gamma GmbH | 1950.00 | open |
INSInsurance— Excel / CSV▾
Raw inputExcel export · inconsistent formatting
| Policy-ID | Inhaber | Art | Prämie | Beginn |
|---|---|---|---|---|
| PKV-26-001 | Dr. Schneider J. | KV | 4200.00 | 01.01.2026 |
| PKV-26-002 | Müller Sabine | KV | 1980.00 | 01/01/26 |
| HV-26-001 | Ritter GmbH Co KG | HV | 38500.00 | 15.01.26 |
| HV-26-002 | Bauer AG | HV | — | 01.03.2026 |
Structure this document
Insurance Policy Extract4 records structured
Type codes expandedDates to YYYY-MM-DDStatus translatedColumns to snake_case
Structured outputvalidated
| policy_id | policy_holder | policy_type | annual_premium_eur | start_date | flag |
|---|---|---|---|---|---|
| PKV-26-001 | Dr. J. Schneider | Private Health | 4200.00 | 2026-01-01 | |
| PKV-26-002 | Sabine Müller | Private Health | 1980.00 | 2026-01-01 | |
| HV-26-001 | Ritter GmbH & Co. | Property Insurance | 38500.00 | 2026-01-15 | legal entity name inferred |
| HV-26-002 | Bauer AG | Liability Insurance | null | 2026-03-01 |