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AI Automation Development

The AI automation development company that handles the judgement, not just the clicks

BinaryBrill is an AI automation development company that builds the intelligent components which take a specific task off a person's desk — reading a document, extracting the fields, classifying a ticket, deciding the clear cases and queuing only the doubtful ones. You get AI workflow automation services and intelligent process automation development from in-house senior engineers who measure accuracy before they trust a component with the task.

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A senior engineer replies within 24 hours — not a sales rep.

Why the task is still done by hand

The task needs judgement, so a macro can't touch it

The reason someone still keys those invoices by hand is that no two arrive in the same layout, and deciding which number is the total takes reading. A rules engine or a screen macro breaks the first time the input varies, which is why the simple automations got abandoned and the manual step came back. Replacing the judgement, not just the keystrokes, is the whole job.

A confidently wrong answer writes bad data into the system of record

An extraction component that guesses the wrong amount and posts it looks exactly like one that got it right — until reconciliation weeks later. A fluent, plausible mistake is more expensive than an obvious failure, because nobody catches it at the time. Without a confidence signal and a check, you have automated the error as well as the work.

Real inputs don't look like the sample you tested on

The demo ran on three clean PDFs. Production sends scans at an angle, a photo taken on a phone, a two-column layout, a language you didn't plan for, and a form somebody filled in with a note in the margin. A component that only handles the tidy case quietly fails on exactly the inputs that were hardest to do by hand anyway.

There's no home for the cases the model isn't sure about

The uncertain 5% is where the risk lives. Auto-process everything and you accept the errors; review everything and you have saved nobody any time. Without a confidence threshold that sends the clear cases straight through and routes only the genuinely ambiguous ones to a person, the automation is either reckless or pointless.

How an AI automation development company should build task automation

An automation system here is the intelligent component that does one task — read this document, classify this ticket, decide this case — to a measured accuracy. It is not the workflow that strings those tasks together across your systems; that orchestration layer is AI process automation, and these components are what plug into it. We build the component and prove it works before you trust it with the task.

Automate the judgement, then measure it

We build a graded test set from your own examples — the ordinary cases, the awkward ones and the ones your team argues about — and score the component against it from the first week. "Is it accurate enough?" becomes a number you can inspect, not an impression from a demo that ran three times.

Confidence thresholds decide straight-through versus review

Every output carries a confidence signal. Clear cases process automatically; anything below the threshold routes to a person with the document, the extracted fields and the reason for the doubt on one screen. You get throughput on the bulk and a human on the genuinely uncertain minority, which is the only split that both saves time and stays safe.

Structured, validated output instead of free text

The component returns data against a schema, validated on the way out, so the next system gets typed fields it can rely on rather than prose it has to parse and hope. Malformed results are repaired or retried before anything is written, and a value that fails validation is treated as a low-confidence case, not passed on regardless.

The component slots into your workflow, not around it

It writes back into your ERP, CRM or accounting system through the API, with idempotency so a retry never duplicates a record. When several of these components are chained across systems with approvals and exception handling, that is process automation — and we build these to drop cleanly into that orchestration rather than to run as an island beside it.

What this covers

Pick the piece you need, or bring us the problem and we'll tell you which applies.

Document & Invoice Processing

The component that reads an incoming document — invoice, form, contract, scanned paperwork — and returns the fields your systems need as validated data. This is intelligent document processing done properly: OCR and layout handling for the messy real inputs, extraction into a schema, and a confidence score on every field rather than a single trust-me answer.

OCR and layout handling for scans, photos, multi-column pages and mixed formats
Extraction into a defined schema, validated on the way out, with per-field confidence
Straight-through processing for clear documents and a review queue for doubtful ones
Write-back into the ERP or accounting system by API, with idempotency so retries never duplicate

Email & Ticket Triage and Classification

The component that reads an inbound email or support ticket and decides what it is, how urgent it is and where it should go. It replaces the manual sorting that eats the first hour of a team's day, and it flags anything it can't categorise confidently instead of guessing and misrouting it.

Intent and category classification tuned on your own historical tickets, not a generic taxonomy
Priority and sentiment scoring so urgent cases surface without a person reading every message
Routing to the right queue, team or system with the reasoning attached for review
A confidence threshold that flags ambiguous messages for a human rather than misfiling them

Data Entry & Enrichment

The component that does the copy-from-here-into-there work between systems and fills the gaps while it's at it — matching a record, normalising a format, looking up a missing field, deduplicating an entry. It is business automation with AI applied to the small repetitive keying that never justified a dedicated tool but adds up to a role across a department.

Record matching and deduplication that catch near-duplicates a simple key comparison misses
Normalisation of inconsistent formats — addresses, dates, product codes — into your standard
Enrichment from internal and external sources, with the source recorded against each value
Validation so a filled-in field that fails a check becomes a flag, not a silent bad write

AI Decisioning & Human-Review Queue

The component that makes a repeatable call — approve, reject, route, flag — on the clear cases and hands the rest to a person. The value is not that it decides everything; it's that it decides the obvious majority to a measured accuracy and gives your team a fast queue for the genuinely uncertain minority.

A confidence threshold agreed with you, so straight-through and review split where you want it
A review interface that shows the case, the proposed decision and the reason on one screen
Every decision logged with the inputs it saw and the version that made it, for later audit
Threshold tuning after launch as the true straight-through and error rates become visible

Content Generation Components

The component that drafts the repetitive text a task produces — a reply, a summary, a description, a first-pass document — grounded in your own content so it stays inside your facts and tone. It is built as a component with validation and a review step, not a free-running writer, because generated text that goes out unchecked is how the tone and the facts drift.

Drafting grounded in your own content and style rules rather than the model's training
Structured output where the text feeds another system, validated before it is used
A review or approval step on anything customer-facing, with the draft clearly marked as one
Evaluation against examples your team approves, so quality is scored rather than assumed

The stack we build on

Chosen to fit the problem — not because it's what we used last time.

Models & language providers

Claude
GPT-4 class models
Gemini
Llama
Mistral
AWS Bedrock
Azure OpenAI Service

Document AI & OCR

Amazon Textract
Google Document AI
Azure Document Intelligence
Tesseract
LayoutLM

Classification & retrieval

scikit-learn
spaCy
Hugging Face Transformers
Postgres with pgvector
Qdrant
LangChain

Serving & queues

Python
FastAPI
Celery
Redis
RabbitMQ
REST APIs
Webhooks

How we'll work together

Every stage ends with something in your hands — not a status update.

01
Task audit and accuracy target
We look at the actual task and its inputs — the real documents, the real tickets, the variety and the edge cases — and agree what accuracy the component has to reach to be worth trusting, plus where the uncertain cases will go. Often this reveals that one task is really three, each needing its own component.
You get: A task specification with the input variety catalogued, an agreed accuracy threshold, and a defined review path for cases that fall below it.
02
Ground truth and baseline component
We assemble a labelled set your own team validates — correct extractions, correct classifications, correct decisions — then build the simplest component that could work and score it. The baseline is frequently closer to acceptable than expected, which changes the budget conversation early.
You get: A validated ground-truth dataset signed off by your subject-matter experts, and a running baseline component with a scoreboard your team can read.
03
Build against the score
Prompt structure, OCR pre-processing, model choice, feature engineering, threshold tuning — each is an experiment with a measured result. We keep what moves the accuracy number and the straight-through rate, and discard what merely looked better in a demo.
You get: The component meeting the agreed accuracy threshold, with confidence scoring wired in and an experiment log showing what was tried and how it scored.
04
Roll out with straight-through and a review queue
The component runs in parallel with the manual process for a defined period so discrepancies surface while the old path is still there to catch them. Clear cases go straight through; the rest land in a review queue built for speed. Only once the two agree does the manual step retire.
You get: Production deployment, a parallel-run comparison report, a review-queue interface, and dashboards for accuracy, straight-through rate, exceptions and time saved.

Where we've applied this

Finance

Field extraction from invoices, statements and remittances into the ERP, with a confidence threshold deciding what posts automatically and what an analyst checks.

Insurance

Claim-document classification and first-notice triage, so each submission is read, categorised and routed to the right handler without someone sorting the inbox by hand.

Logistics

Line-item extraction from delivery notes, bills of lading and customs paperwork, normalised into structured fields before they reach the operations system.

Healthcare

Intake-form extraction and referral triage that route paperwork to the right department, kept strictly clear of any clinical judgement.

Legal & professional services

Contract clause extraction and document classification that pull key terms into structured fields and file each document where a fee earner can find it.

Customer support

Ticket classification, priority scoring and routing that read an incoming message and send it to the right queue, with low-confidence cases flagged rather than misfiled.

Questions buyers ask us

What's the difference between AI Automation Systems and AI Process Automation?

This page is about the components — the AI pieces that do one task each: read a document, classify a ticket, decide a case, enter data. AI process automation is the layer above it: the end-to-end workflow that moves a whole case across your ERP, CRM, email and databases, deciding which steps run automatically, where the approvals sit and how exceptions are handled. Put simply, we build the intelligent components here, and the process that strings them together is process automation. Most real projects need both, and the two teams build to the same interface so a component drops straight into the process.

How do I know if a task is a good candidate for this?

Good candidates are high-volume, repetitive tasks that currently need a person because they involve reading or judgement — not because a rule couldn't be written. Reading invoices, sorting tickets, classifying documents, matching records: all strong fits. If the task is a fixed rule you could write down in full, an ordinary script is cheaper and more reliable than a model, and we'll tell you so. If the task happens twice a month, the automation won't pay for itself. Volume plus genuine judgement is the sweet spot.

Do you build the component from scratch, or integrate an existing tool?

Both, and choosing is part of the job. For common tasks like standard invoice extraction, a good off-the-shelf document-AI product may fit, and integrating it is faster and cheaper — we'll recommend that when it's true. We build custom components where your inputs are unusual, the accuracy bar is higher than a generic tool reaches, the data is proprietary, or the task is specific to how you work. Often the answer is a hosted model for the generic part and custom work only where it earns its cost.

How accurate will it be, and can you tell me before we build?

Not honestly, before seeing your data. What we commit to is telling you early: the baseline and evaluation in the first phase give a measured accuracy on your own examples within weeks, and if that number is too low to be useful for your task we'll say so rather than push on. A component with a known accuracy and a review queue for the uncertain cases is far more useful than a confident one with no track record.

What drives the cost, and how long does it take?

Build cost is driven by the variety of your inputs (three clean formats is cheap; twenty messy ones is not), the accuracy bar, and how many systems the component writes back to. Running cost is mostly per-item inference, which we model during design so you can price the saving honestly. On timeline, a scored baseline on your data is usually a few weeks; a production component with confidence scoring, a review queue and monitoring is typically a few months, depending mostly on input variety and data readiness.

Will our documents and data be used to train someone else's model?

No. Your data stays within your environment and accounts wherever the architecture allows, and it isn't used for anything outside your project. Where a hosted model provider is in the design, we tell you exactly which one, what leaves your infrastructure, and their retention and training terms before you approve it — and enterprise tiers that exclude your data from training are usually part of that recommendation.

When is this the wrong approach?

When the task is a stable, writable rule — use a script. When the cost of any error is too high to accept even a small rate and the volume is too low to justify a careful review queue — keep it manual. When you actually need the whole process automated across systems rather than one task done well, you need process automation, and a lone component won't get you there. And when there's no way to tell a good output from a bad one, the task isn't ready to automate yet. We'd rather say this in discovery than bill you to discover it.

What happens to the cases the component isn't confident about?

They go to a human-review queue built for speed — the case, the component's proposed answer and the reason for the doubt on one screen, so a reviewer confirms or corrects in seconds rather than starting from scratch. You set the confidence threshold, so you control the split between straight-through and review, and we tune it after launch as the true rates become visible. Corrected cases feed back in to improve the component over time.

Who writes the code, and where are they based?

Our own in-house engineers in Sahibzada Ajit Singh Nagar, Punjab — 45+ of them, with over a decade of combined delivery experience, delivering for clients in 15+ countries. Nothing is subcontracted; you own the code, repository and pipeline from day one. You meet the engineers who will be on your project before you sign, and the person demonstrating the work each sprint is the person who built it.

Tell us which task you want off your team's desk

Describe the document, the ticket or the repetitive decision, and roughly how often it happens. A senior engineer replies within 24 hours with a straight read on whether it's a good candidate, what accuracy is realistic, and where the review queue should sit — including when the honest answer is a script instead.

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