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AI Process Automation

The AI process automation company that automates the whole process, not one step of it

BinaryBrill is an AI process automation company that takes a whole business process end to end — mapping it, orchestrating the steps across your ERP, CRM, email and databases, and combining deterministic workflow with AI only where a step genuinely needs judgement. You get end-to-end process automation with AI and AI operations automation services from in-house senior engineers who know which steps should stay deterministic and which shouldn't run without a person.

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

Why the process breaks between the steps, not within them

Each system was automated; the process still isn't

Purchasing has a tool, finance has a tool, the warehouse has a tool, and a person still spends the day carrying data between them, chasing an approval by email and re-keying an exception. The individual steps are automated and the process is not, because nobody owns the seams — and the seams are where the delay and the errors actually live.

AI was applied to a step that never needed it

A step with a clear, stable rule got a language model bolted onto it, so a decision that should be deterministic and instant is now slower, more expensive and occasionally wrong for no reason. The opposite of skill in this work is putting AI everywhere; the skill is knowing the handful of steps that genuinely need judgement and leaving the rest as plain, reliable logic.

The exceptions are the process, and nobody automated those

The happy path was scripted and the 20% of cases that don't fit it — the mismatch, the missing document, the out-of-policy request — still fall out to a human with no structured way to handle them. An automation that only covers the clean cases leaves the hard, slow work exactly where it was and adds a handoff on top.

When it runs unattended, nobody can see or explain it

Once a process runs on its own across five systems, someone will ask where a stuck case is, why an approval was skipped, or what happened on the run that went wrong at 2am. Without a trace of every step, an audit trail on the decisions and monitoring on the running process, the honest answer is that you don't know — and that is when the process gets switched back to manual.

How an AI process automation company should automate a process

Process automation is the orchestration layer: the mapped sequence of steps that moves a case across your ERP, CRM, email and databases from start to finish. The individual judgement steps within it — read this document, classify this case — are AI automation components, and this is the layer that strings them together, connects the systems and handles everything between them. The skill is deciding which steps need AI, which stay deterministic, and where a person has to sign off.

Map the real process before automating any of it

We map the process as it actually runs, with the people doing the work — including the informal steps, the exceptions and the workarounds that never made it into a policy document. Automating the version that exists on paper is how projects ship something nobody can use. The map is where the real decisions about what to automate get made.

AI only where a step needs judgement

Each step is classified: deterministic logic where the rule is clear, an integration where it's a system-to-system move, and an AI component only where the step genuinely requires reading or judgement. Most steps are not AI, and that is the point — deterministic steps are faster, cheaper and easier to trust, so the model earns its place only where nothing simpler will do.

Approvals, audit trails and human-in-the-loop where accountability sits

Human checkpoints go where the responsibility genuinely lives — the payment, the customer-facing action, the out-of-policy exception — not uniformly across every step. Each decision is recorded with what it saw and who approved it, so the process is auditable end to end and a regulated step always has a named person behind it.

Exception handling and monitoring on the running process

Exceptions are designed for, not left to fall out to a person: a defined path for the mismatch, the missing field, the timeout, with retries and escalation. The running process is instrumented so you can see every in-flight case, its stage and its age, with alerting on stuck cases and SLAs — because an unattended process you can't watch is one you'll end up switching off.

What this covers

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

End-to-End Process Mapping & Design

Before any automation, we map how the process really runs and design the automated version around it. This is the half of end-to-end process automation with AI that gets skipped and then sinks the project — the informal steps, the exceptions and the workarounds that live in people's heads rather than any policy document.

Mapping with the people who do the work, exceptions and workarounds included
Each step classified as deterministic, an integration, or a genuine judgement step
A design that marks where approvals sit and where a case can safely run unattended
An honest view of what shouldn't be automated at all, before you spend on building it

Cross-System Orchestration & Integration

The engine that moves a case across your systems — ERP, CRM, email, databases, SaaS APIs — and keeps track of where every case is. This is the backbone that automating business processes with AI depends on, and we build it deterministic and reliable first, because a shaky integration layer makes every downstream failure impossible to diagnose.

Durable workflows that survive restarts and know the exact state of every in-flight case
Integration through APIs and events, with scheduled file exchange only where nothing better exists
Retries, timeouts and compensation so a half-finished run doesn't leave data inconsistent
A deterministic backbone the AI steps plug into, not the other way round

RPA Combined with AI

Deterministic automation for the steps with clear rules, robotic process automation where a legacy system offers no API, and AI only on the steps that genuinely need judgement. The point is the mix: not every step should be a model, and knowing which few need one — and which should stay plain, fast, testable logic — is where the reliability comes from.

Rules-based automation for the deterministic steps, kept simple and easy to trust
RPA against legacy interfaces where no API exists, isolated so it's easy to replace later
AI components on the judgement steps only, behind validation and confidence thresholds
A clear boundary between what runs on logic and what runs on a model, documented per step

Approvals, Audit Trails & Human-in-the-Loop

The checkpoints, sign-offs and audit trail that make an unattended process safe to run. Human-in-the-loop is placed where accountability genuinely sits — the payment, the customer action, the out-of-policy exception — not uniformly, so people spend their attention where it matters and the rest flows through.

Approval gates on the steps that move money, contact customers or can't be undone
A full audit trail of every step, decision and approval, retained for review
Exception queues with the case and its context assembled for a fast human decision
Role-based access so each approval sits with a named, authorised person

AI Operations Automation & Monitoring

Running a process in production is its own discipline, and this is where AI operations automation services live: monitoring every in-flight case, meeting SLAs, catching stuck runs and reporting on how the process actually performs. An automated process you can't watch is one you'll switch back to manual the first time it's questioned.

A live view of every in-flight case, its stage and its age against the SLA
Alerting on stuck cases, rising exception rates and steps breaching their time budget
A kill switch and rate caps per step, so a misbehaving run can be stopped fast
Reporting on cycle time, exception rate and throughput per process, not per tool

The stack we build on

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

Orchestration & workflow

Temporal
Apache Airflow
n8n
Camunda
Celery
Prefect

Integration & RPA

REST APIs
Webhooks
Apache Kafka
Zapier
UiPath
Playwright

AI for the judgement steps

Claude
GPT-4 class models
LangChain
LangGraph
Model Context Protocol
FastAPI

Observability & operations

OpenTelemetry
Grafana
Prometheus
Sentry
PostgreSQL
Redis

How we'll work together

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

01
Process mapping and step classification
We map the process end to end with the people who run it, then classify every step: deterministic logic, a system integration, or a genuine judgement step that needs AI. This is where we decide what to automate, what to leave manual, and where the approvals belong — and it frequently shrinks the AI footprint to a few steps.
You get: A process map of the real workflow with each step classified, the systems each step touches, and a written list of what stays deterministic and what stays under human approval.
02
Integrations and deterministic backbone
We build the connections to your systems and the deterministic workflow first — the reliable, testable backbone that moves a case from step to step with proper error handling. Getting this solid before any AI is added means later failures are unambiguous rather than lost between the model and the plumbing.
You get: A tested integration layer with documented connections to each system, and a running deterministic workflow with state tracking, in a sandbox that operates on copies rather than live records.
03
Add the judgement steps and the checkpoints
The AI components go into the steps that need them, behind validation, with confidence thresholds routing uncertain cases to the human checkpoints placed in step one. The whole process runs against real scenarios — the normal path, the exceptions and the awkward cases — before it touches anything live.
You get: The end-to-end process running in the sandbox with AI steps, approvals and exception paths in place, and a scenario report covering the happy path, exceptions and edge cases.
04
Roll out with monitoring and SLAs
The process goes live at limited volume alongside the existing way of working, with monitoring on every in-flight case, alerting on stuck ones and a kill switch. Scope and volume widen on the evidence. If a class of case never earns unattended running, it stays under approval, and that is a legitimate end state.
You get: Production deployment with per-step controls and a kill switch, an operations dashboard showing in-flight cases and SLAs, audit-trail retention, and a documented escalation procedure.

Where we've applied this

Finance

Procure-to-pay run end to end — purchase request, approval, matching against the ERP and payment — with AI only on the invoice-reading step and a person on the payment.

Insurance

Claims from first notice to settlement, orchestrated across intake, policy and payment systems, with adjuster checkpoints on the decisions that carry liability.

Logistics

Shipment exception handling across the TMS, ERP and carrier APIs — gathering status, checking the contract, drafting the update and holding dispatch for a coordinator.

Healthcare

Patient onboarding and prior-authorisation processes across scheduling, records and payer systems, with the administrative steps automated and clinical judgement left to people.

Manufacturing

Maintenance and procurement work-order processes across the ERP and supplier systems, correlating the trigger, the parts and the history, then holding release for a planner.

Professional services

Employee onboarding orchestrated across HR, IT provisioning and finance, so a new starter's accounts, equipment and payroll are set up without a checklist chased by hand.

Questions buyers ask us

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

Process automation is the whole workflow — the end-to-end sequence that moves a case across your ERP, CRM, email and databases, with approvals, exception handling and monitoring on the running process. AI automation systems are the individual intelligent components within it, each doing one task: reading a document, classifying a ticket, deciding a case. Put simply, we build the process that strings the steps together here, and the AI components that do the judgement steps are automation systems. Most real projects need both; the two are built to the same interface so a component drops cleanly into the process.

Does every step of the process become AI?

No, and it shouldn't. Most steps in a well-built process are deterministic logic or plain system-to-system integration, because those are faster, cheaper and easier to trust. AI goes only on the handful of steps that genuinely need reading or judgement. A large part of our job is talking teams out of putting a model on a step that has a perfectly good rule — that's added cost, latency and risk for nothing.

How is this different from RPA on its own?

Traditional RPA automates deterministic steps, often by driving a user interface, and it's brittle when a screen changes or a case doesn't fit the script. We use RPA where it's the right tool — usually a legacy system with no API — but combine it with proper API integration for reliability and with AI for the steps that need judgement, all inside an orchestration layer that tracks state and handles exceptions. RPA alone automates clicks; this automates the process, exceptions included.

Which processes are good candidates to automate?

High-volume, repeatable processes that cross several systems and currently rely on a person to carry work between them — procure-to-pay, claims intake, onboarding, order exception handling. The stronger the case, the more it involves the same steps in the same order with a clear owner. Processes that change shape every time, or that are really a series of one-off judgement calls, are a poor fit, and we'll say so during mapping rather than build something that fights the reality.

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

Build cost is driven mostly by the number of systems the process touches and how good their interfaces are — integration is the bulk of the work, not the AI. The number of genuine judgement steps and the depth of exception handling add to it. Running cost is a mix of orchestration infrastructure and inference on the AI steps, which we model during design. On timeline, process mapping and the integration backbone are usually a few weeks to a couple of months; a full process with AI steps, approvals and monitoring is typically several months, depending on system access.

Will our data stay private, and who can see what the process did?

Your data stays within your environment and accounts wherever the architecture allows, and it isn't used to train anyone else's model. Where a hosted model provider sits on a judgement step, we tell you exactly which one, what leaves your infrastructure and their retention terms before you approve it. Internally, the audit trail records every step and decision with role-based access, so you can see and explain what the process did on any case.

When is full process automation the wrong call?

When the process changes shape every time it runs, automating it fights reality and manual handling is genuinely better. When only one task in an otherwise fine process is the pain, you want an automation component, not a whole orchestration project. When the systems involved have no usable interfaces, the integration cost may outweigh the saving, and we'll say so early. And when a process is mostly high-stakes judgement, the honest design keeps people making the decisions and automates only the assembly around them.

What happens when a case gets stuck or a step goes wrong at 2am?

The orchestration layer tracks the state of every case, so a stuck one is visible rather than lost. Monitoring alerts on cases that breach their SLA or fail a step, exceptions route to a defined queue instead of silently dropping, and there's a kill switch and rate caps per step. When something does go wrong, the full trace of that run — every step, decision and system call — means the review starts with facts, and the failure becomes a scenario the process is hardened against.

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 process you'd hand over first

Describe the process, the systems it crosses and where a case tends to get stuck. A senior engineer replies within 24 hours with a view on which steps should be automated, which should stay deterministic, where the approvals belong, and whether the systems will integrate cleanly.

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