Services / AI Engineering
We turn documents into structured, source-linked data that your team can review and your systems can use. Built around your document types, business rules and the exceptions that matter.
Explicit missing values. Evidence-backed fields. Controlled system updates.
The business case
The real work is getting the right information into the right system without carrying mistakes downstream. We build document workflows that reduce rekeying, expose discrepancies and let people focus on exceptions, rather than treating extracted text as a finished business outcome.
Recurring documents, repeated data entry, defined target fields and a measurable review bottleneck. Start with a stable document family and a process owner who can explain what a correct record looks like.
Structured exports, standard forms or native system integrations may avoid extraction altogether. Low volumes, highly ambiguous records or missing review capacity can make full automation the wrong investment.
Example engagements
These are illustrative scopes, not claims of customer deployments. We assess document quality, authorised data access and downstream requirements before proposing automation.
Extract supplier details, invoice references, currencies, totals and line items, then reconcile against purchase orders or master data.
Keep payment approval and bank-detail changes behind independent controls. A readable invoice is not proof that it is legitimate.
Measure: Critical-field accuracy, exception rate and reviewer time per invoice.
Locate renewal dates, obligations and selected clauses, with references to the original page and passage.
Flag absent or conflicting language. Legal interpretation and consequential decisions remain with qualified reviewers.
Measure: Evidence correctness, missed obligations and review effort.
Read packing lists, delivery notes and shipping documents, linking quantities and identifiers across a shipment.
Resolve unit, reference and quantity conflicts before updating operational systems. Do not infer missing customs information.
Measure: Matched shipments, discrepancies caught and reconciliation time.
Turn authorised submissions into structured records, identify missing fields and prepare a review queue.
Extraction is not eligibility assessment. Identity, consent and consequential application decisions need separate controls.
Measure: Required-field completeness, correction rate and processing time.
Capture selected tables, readings and observations from inspection reports, certificates or equipment records.
Retain units, dates and source context. Safety-critical findings require specialist review rather than model interpretation alone.
Measure: Table fidelity, unit errors and exceptions detected.
Classify an authorised archive and extract metadata for a searchable document or records system.
Preserve access permissions and lineage. A new index must not make restricted content visible to a wider audience.
Measure: Classification quality, metadata coverage and permission integrity.
Extraction architecture
We combine deterministic parsing, OCR and document or language models where they add value. The output is checked against a schema and your business rules, with a clear path for uncertain records. Extraction and permission to act remain separate.
Accept approved channels such as uploads, mailboxes or storage integrations. Assign a document ID and version, check file type and size, scan for malware and handle encrypted or unsupported files explicitly.
Use native text where reliable; apply OCR or document models to scanned pages. Preserve page coordinates, reading order and table structure, and detect poor-quality input before extraction.
Route document types to an agreed schema. Use parsers, document models or bounded multimodal extraction as appropriate. Keep missing values explicit; a schema-valid answer can still be factually wrong.
Check dates, units, currencies, arithmetic and required fields in code. Match references against authorised systems. Capture conflicting sources and treat supplier bank details as unverified until independently checked.
Route exceptions and sensitive fields to a reviewer with source evidence. Write only accepted records using scoped APIs, duplicate protection and reconciliation for uncertain outcomes.
Record source and pipeline versions, checks, corrections and downstream results with agreed retention. Add representative failures to the evaluation set before changing extraction logic.
A worked document journey
An illustrative accounts-payable workflow shows how extracted information becomes a checked record while financial authority stays with your existing controls.
Receive the invoice through an approved channel, establish its source and version, and identify potential duplicates without assuming that two similar files are identical.
Read supplier and invoice references, dates, currency, totals and line items. Link fields to the source where supported, and keep unclear or missing values unresolved.
Check arithmetic, supplier records and purchase-order references. Route mismatches, ambiguous tax treatment and bank-detail changes to authorised people; never silently repair a conflicting total.
Submit the reviewed data to the agreed accounting system, verify the returned record ID and reconcile uncertain writes. Payment approval remains a separate process.
Test credit notes, currency ambiguity, partial deliveries, duplicate references, missing pages and a timeout after record creation. The final system record matters more than a convincing extraction preview.
Document realities
Digital PDFs, phone photos, scans and spreadsheets behave differently. We assess the documents you actually receive, not just a clean sample supplied for a demonstration.
Prefer reliable embedded text when possible. Check for broken encodings or incorrect reading order, and use OCR when the text layer is missing or misleading.
Preserve row associations, headers, units and continuation pages. Test merged cells, repeated headers and totals separately; flattening everything into prose loses important relationships.
Benchmark required scripts, mixed-language documents, handwriting and local number formats. Provider coverage does not establish acceptable quality on your documents.
Detect unfamiliar layouts, blur, skew, clipped pages and missing attachments. Give users a clear resubmission or manual-review path rather than forcing a plausible output.
Human review & confidence
Confidence scores differ across providers and fields. A language model's self-reported certainty is not a calibrated probability. We combine measured field behaviour, source evidence and deterministic checks to decide which records need review.
Attach page references and bounding boxes where the extraction method supports them. Distinguish quoted evidence from normalised values, derived calculations and model interpretation.
Set acceptance rules by field and business impact using held-out samples. A correct address cannot compensate for an incorrect amount; sensitive fields may always require a person.
Place the source beside extracted fields, highlight unresolved checks and support corrections with a reason. Preserve who accepted the record and the exact version they reviewed.
Reviewer edits are useful evidence, not automatic training labels. Curate examples, protect personal data and evaluate changes before release; do not silently retrain on client documents.
Security & data handling
Incoming files may contain sensitive records, malicious content or instructions aimed at a model. Security controls belong around the entire pipeline, not just in the extraction prompt. We work with your security and legal teams; this is not a blanket compliance guarantee.
Validate file signatures, bound decompression and processing resources, and isolate parsers. Avoid executing macros, embedded scripts or links from a document.
Enforce document, field and export permissions in application logic and storage. Restrict service identities and test that one user's or client's documents cannot appear in another's results.
Treat text inside documents as untrusted content. Extraction components do not receive broad tools or authority to change records, send data or override business rules.
Agree retention and deletion for originals, temporary files, extracted values, queues and logs. Check model-provider processing, training terms, region and subprocessors before sending sensitive documents.
Evaluate local OCR and self-hosted models where required. Verify every layer, including telemetry and backups; hosting the application privately does not make external model calls private.
Separate extraction from payment, eligibility, legal or safety decisions. Keep approvals, fraud checks and authoritative rules outside the model, with clear responsibilities.
Evaluation & production readiness
We agree release criteria against a representative labelled set, with held-out documents separated from development examples. Results are reported by document family, input quality and field importance, not one flattering aggregate accuracy number.
Measure missing and incorrect fields, normalisation errors and line-item associations. Define how dates, numbers and acceptable text variations are compared; inspect critical errors separately.
Measure coverage alongside errors in accepted records, exception volume and human correction effort. High automation coverage is not useful if incorrect records escape review.
Test large files, bursts, timeouts, parser failures, provider limits and partial writes. Use bounded retries, dead-letter queues and reconciliation rather than silently dropping failed documents.
Version schemas, parsers, prompts and models. Re-run the labelled set before releases and monitor changes in document mix, review rates and cost, with an agreed rollback path.
Technology stack
These are options we can evaluate, not a mandatory bundle or a claim that every engagement uses them all. Selection depends on representative benchmarks, licensing, data terms, deployment constraints and your team's ability to operate the result.
Select native-text and layout parsers by input format. Review licences, supported formats, sandboxing and fidelity; no parser handles every document reliably.
Compare managed document models and local OCR using your languages, tables and scans. Check region, data terms, page limits and actual field quality.
Evaluate vision-language models for variable layouts and semantic fields. Use schema-constrained output where supported, source evidence and independent validation; model fluency is not accuracy.
Implement typed schemas, explicit normalisation, reference matching, permissions, review state and durable audit records outside the model.
Coordinate asynchronous jobs, retries and review waits; integrate with authorised ERP, CRM or records systems. Choose one suitable execution approach rather than stacking every option.
Build source-linked review screens, field-level benchmarks and privacy-aware traces. Deployment and monitoring fit your hosting and operating requirements.
Retrieval and vector search are optional when the workflow also needs document discovery or question answering. They do not replace exact field extraction, source permissions or validation against authoritative records.
Economics & delivery
Compare total effort per accepted record with the current process, including processing fees, infrastructure, integration and review. A cheaper extraction step is not a saving if your team spends longer finding and fixing its errors.
Agree document families, schemas, downstream actions and review owners. Sample authorised documents across suppliers, languages, formats and input quality.
Output: A bounded scope, data-handling plan and acceptance criteria.
Label representative samples and compare parsing, OCR and model approaches. Establish field accuracy, evidence quality, review load and estimated costs.
Output: A baseline, held-out evaluation set and feasibility findings.
Connect ingestion, extraction, validation, review and one agreed destination. Include duplicates, missing values, failed jobs and uncertain writes.
Output: A reviewable end-to-end workflow, not just a JSON demo.
Run alongside the existing process, examine errors and adjust review rules. Validate permissions, failure recovery and operational responsibilities before expanding automation.
Output: Measured pilot results, known limits and a rollout decision.
Document schemas, integration contracts, evaluation, deployment and incident procedures. Agree support, ownership and the process for new document types.
Output: Agreed source access, runbooks and a change-management plan.
Start with a paid two-week sprint to assess one document family and build an agreed first slice. Scope, access and review points are set before starting. No long-term commitment is required; production rollout may need further work.
Authorised representative samples, a process owner, field definitions and a reviewer who can label and assess results. We also need destination-system access and contacts for security, legal and operational decisions.
Related engineering
Explore language-model applications and practical guidance for human oversight and reliable evaluation.
Questions before you build
OCR reads characters from images. A document workflow also needs layout interpretation, classification, structured extraction, validation, source evidence, human review and system integration. Some clean digital documents need no OCR or language model at all.
No. Quality depends on document variety, input quality and the fields being extracted. We benchmark representative samples, agree acceptance criteria and route uncertainty or sensitive fields to review. We do not promise a universal accuracy figure.
We can assess both, but feasibility and quality vary. We test row relationships, page continuations, required scripts and handwriting on real authorised samples. Difficult input may need resubmission or manual processing.
We assess supported APIs, permissions, data contracts and write behaviour. Accepted records can be routed to the agreed system with duplicate protection and result verification. Direct database writes are not assumed as a shortcut.
Potentially, using local parsing, OCR and suitable self-hosted models. We assess hardware, licences, security, operating cost and achievable quality. Every dependency must meet your requirements, not just the main application.
Not by default. Any training or fine-tuning requires an agreed purpose, permission, data-handling terms and an evaluation plan. We also review the processing and training terms of the selected providers.
We consider pages, OCR and model usage, retries, storage, infrastructure, integration and human review. Cost per accepted usable record is often more meaningful than cost per page alone. Estimates depend on measured document mix and volumes.
An authorised representative sample, target fields, a process owner, review criteria and details of the destination system. A paid two-week sprint can assess feasibility and build an agreed first slice without a required long-term commitment; production rollout may need further work.
Start with the documents
Tell us which documents arrive, what your team needs from them and where accepted data should go. We'll assess the boundaries and a sensible first scope.