What we build
Six building blocks. Most engagements start with one of them and grow into two or three.
Document extraction pipelines
Unstructured input to structured output. Invoices, receipts, bills, driver paperwork, PDFs, scans and photos become fields your systems can read and validate.
Classification and routing
The system decides what a document is before deciding what to do with it, then routes it to the right queue, approver or ERP record.
RAG assistants over your document library
Ask a question in plain language, get an answer bounded to your own files with a citation pointing at the exact source page.
Document generation
The reverse direction. Branded PDF reports, proposals, prescriptions, letters and contracts generated from live data on demand.
Reconciliation and validation logic
Where the document meets the money. Transaction classification, discrepancy detection, manual override with value locking and a full audit trail.
Human-in-the-loop review
Confidence thresholds, approval gates and correction interfaces, so the exceptions reach a person and the routine cases never do.
The documents we have already built for
Every line below is a shipped pipeline, not a capability we are willing to try.
Financial documents
Invoices, bills and receipts extracted, categorized and synced into eight ERP systems including QuickBooks, SAGE and Xero, for a fintech and AI client running 1,200+ documents a day across 2,000+ businesses.
Insurance and compliance paperwork
An “Upload and Go” driver document parser for an insurtech client, feeding a commercial trucking quote wizard with FMCSA prefill, DocuSign e-signature and PCI SAQ-A compliant checkout.
Technical manuals
4,090 indexed product manuals behind AskCW.ai for CompressorWorld, with every answer traceable to its source and no hallucinations.
Photographed and scanned documents
Image-based VIN extraction from a photo of a plate, a dashboard or a printed document for an automotive AI client, plus WebTwain scanner integration in the fintech build.
Research documents and long-form content
A PDF-to-course pipeline that turns uploaded research documents into a structured micro-learning course, and a video-parse-to-brief pipeline for a martech AI client.
Logistics and freight records
An automated reconciliation engine for a logistics and fintech client, catching carrier under- and overpayments across thousands of loads through fuel surcharges and retroactive rate adjustments.
Mixed enterprise sources
PDF, DOCX, TXT, XML feeds and SQL ingested together in Bridge, our own AI agent and RAG platform.
How we deliver a document AI build
Five steps. The second one is the reason the pipeline can be trusted.
We take a real sample of your documents, including the ugly ones: skewed scans, handwritten annotations, three layouts from the same vendor. Accuracy claims made against clean samples are worthless, so we measure against yours.
We define what fields matter, what each one is worth if wrong, and which system is authoritative when two sources disagree. This is the step most vendors skip, and it is the one that decides whether the pipeline can be trusted.
Generation happens inside our open-source boilerplates, so auth, roles, file handling, tests, CI and migrations already exist before the first product line. AI generates into a known structure instead of inventing one.
Every field gets a confidence score and a rule for what happens below the threshold. Approval gates go where the action matters, exactly as they do in Bridge, where critical actions require human sign-off.
Citations on every retrieved answer, a full override and correction log, an accuracy evaluation set you keep, and code you own outright. No lock-in, no proprietary runtime.
Bridge — Document Intelligence Platform
Our own AI agent and RAG platform, in production. It is where the architecture rules on this page were proven before we shipped them into client builds.
Native parsers for PDF, DOCX and TXT alongside XML feeds and SQL, ingested into one corpus.
A hybrid engine combining vector and keyword search, so exact identifiers and fuzzy questions both land.
Generation is strictly bounded to the client dataset — a confident invented answer is architecturally unavailable.
Every answer links back to the exact source document, down to the page.
The agent writes back into live systems through Model Context Protocol, with human approval required on anything critical.
On-premise or inside your VPC, with vendor-agnostic model support and no proprietary runtime.
Document AI we have shipped
Five delivered client pipelines. Names appear where clearance allows.
What this actually fixes
Read it as a diagnosis. If the left column describes your week, the right column already exists somewhere in our portfolio.
How we keep it accurate
Three rules that survive contact with production.
Bounded generation
The model answers from your data or it does not answer. Bridge enforces this at the platform level: generation is strictly bounded to the client dataset, so a confident invented answer is architecturally unavailable.
Citations as a requirement
Every retrieved answer links back to the exact source document. When a controller or an auditor asks where a number came from, the system points at the page instead of shrugging.
Source hierarchy over model judgment
Numbers, money and configuration come from the system of record. The model reads and explains, it does not calculate. We learned this the expensive way on a live product, and it is now a standing rule.
Three ways to start
Pick the smallest one that answers your question.
Document AI audit, 1 to 2 weeks
We take your real documents, measure what current extraction accuracy would be, map the workflow around them, and give you a phased plan with an estimate. You keep the analysis whether or not you build with us.
Fixed-scope MVP, 4 to 8 weeks
One document type, one workflow, one integration, in production. Nine of our delivered MVPs carry a checkable number, including a $3,375 AI MVP delivered about 17% under estimate.
Ongoing product partnership
For platforms where documents are the product. Our longest document-automation engagement ran 71 months.
What we build it with
Vendor-agnostic by design, switched on cost and performance.
Models and AI services
OpenAI, Anthropic, Google, open-source Llama. Vendor-agnostic by design, switched on cost and performance.
Retrieval
LlamaIndex, hybrid vector plus keyword search, PostgreSQL with pgvector.
Orchestration
Model Context Protocol for tool and action layers, n8n for pipeline workflows.
Application
NestJS, Next.js, React, TypeScript, PostgreSQL.
Document handling
Native PDF, DOCX, TXT, XLSX and XML parsers, WebTwain scanner integration, Puppeteer for generated PDF output.
Infrastructure
AWS, GCP, Docker, CI/CD from day one, on-premise and VPC deployment where the data cannot leave.
AI Native Development
We in Brocoders reorganized the company around AI rather than bolting it on. Product managers build working frontends with AI, engineers own backend and architecture, and QA, security and CI were rebuilt around AI tooling. The reason this does not produce an unmaintainable codebase is our open-source boilerplates at bcboilerplates.com, cloned by roughly 500 developers a month, which fix architecture, auth, file handling, tests, CI and migrations in advance. 193 hours on the React side and 126 on the NestJS side are decided before the first product line exists. A security pass over AI-generated code is part of our definition of done.
We packed this method into the Fieldera product and built a ServiceTitan alternative in 7 days.
Our older portfolio builds predate this reorganization and we do not claim they were delivered this way. They are why we know what to architect. The AI-native model is how fast we get there now.
Where our document work already lives
Correctness and audit trail are the hard part in these industries, not the interface.
Fintech and accounting
- Pre-accounting automation into eight ERPs
- Freight invoice reconciliation with retroactive rate adjustments
- KPI reporting with generated PDF output
Insurance and logistics
- Driver document parsing inside a self-serve quote flow
- FMCSA integration
- Compliance-safe payment handling
- Carrier payment discrepancy detection across thousands of loads
Industrial, ecommerce and support
- Thousands of product manuals turned into an assistant that answers buyers accurately
- Intent-based product search mapping plain-language requests to specific SKUs
Clients' reviews
5.0 on Clutch across 30 reviews. Around 60% of our engineers are senior. Estonia HQ, EU entity, full code ownership.
FAQ
AI document processing turns unstructured documents into structured, usable data. A pipeline classifies the document, extracts the fields that matter, validates them against a source system, and pushes the result into the software that runs the process. Modern systems combine OCR, language models and retrieval, with confidence scoring deciding what a person still needs to see.
Accuracy depends entirely on your documents, not on the model. That is why we start with an audit against your real files rather than a demo against clean ones. A well-designed pipeline is measured field by field, with each field carrying a confidence threshold and a defined fallback, so the number that matters is not overall accuracy but how reliably the system escalates the cases it should not decide alone.
Not if it is built correctly. We bound generation strictly to your dataset, attach a citation to every retrieved answer, and take all numerical, financial and configuration values from the source system rather than the model. In one delivered assistant answering from 4,090 product manuals, every answer is traceable to its source.
Yes. We have shipped scanner integration in a high-volume accounting platform and image-based extraction from photos in a consumer product, including pulling a VIN from a photograph of a plate, a dashboard or a printed document.
Yes. Bridge, our AI agent and RAG platform, deploys on-premise or into your VPC, and we build client systems the same way when the data cannot leave. Vendor-agnostic model support means you are not locked to one provider.
A document audit takes 1 to 2 weeks. A fixed-scope MVP covering one document type and one integration typically runs 4 to 8 weeks. A comparable AI build we delivered went from six discovery workshops to production in 8 weeks.
Yes, completely. Our boilerplates are open source and public, there is no proprietary runtime, and no lock-in.
Schedule a call or send us a message
We are thrilled about the opportunity to provide software development services for your business
Rodion Salnik
CTO and Co-founder at Brocoders
Pick a date that works for you to see available times to meet with me and discuss your project needs. Looking forward to meeting you!