AI business process automation that gives your team back the hours lost to repetitive work, with gains measured before and after. For operations leaders.
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DIAL Data Foundation (The Hackett Group):
Automated data cleansing and deduplication
ADT Solar:
Salesforce, DocuSign and Airplane integrated from sale to installation
SciPlay:
Business teams launch reward rules without engineering involvement
20+years of software engineering; US enterprise clients since 2022
DIAL Data Foundation (The Hackett Group): automated data cleansing and deduplication
ADT Solar: Salesforce, DocuSign and Airplane integrated from sale to installation
SciPlay: business teams launch reward rules without engineering involvement
20+ years of software engineering; US enterprise clients since 2022
The team retypes data from emails, PDFs and spreadsheets into the ERP every day, and the errors only surface at month-end close.
The process depends on two or three people who know the exceptions; when one goes on vacation, the queue stops.
Someone connected an autonomous agent such as OpenClaw to the company email, and nobody knows what it is allowed to do or what it has already done.
Leadership asks for an AI plan, but nobody can say which process to automate first or what it would return.
You know which process to automate first: the candidates, with volume, time spent, error rate and rules, ranked by expected return and by the risk of automating them.
No manual retyping: workflows read PDFs, emails and spreadsheets, extract the required fields and check the result against company rules and master data.
LangGraph workflows that query, create and update records in the ERP, CRM and other systems through APIs, pause at approval points and resume where they stopped.
A review queue for out-of-rule or low-confidence cases, with the context gathered so a person can decide without redoing the work.
Recurring tasks in your team's channel (Slack, WhatsApp, email), run by OpenClaw or Hermes Agent (Nous Research), with per-tool permissions, scheduled tasks and auditable persistent memory.
In multi-step processes the agent drafts a plan, hands each part to a subagent with its own context and permissions, and checks the result before moving on.
Connectors between the systems involved, queues with automatic retries and a log of every run, so that no step fails silently.
Every run produces a trace with input, decision, tools, tokens and cost; the dashboard shows volume, time per case and exception rate against the baseline.
When a process depends on a forecast (demand, lead time, delay risk), we train supervised learning models on your history and validate them on data the model has not seen.
See also: AI customer service agents, AI agent development, and marketplace and payment integrations.
DIAL Data Foundation: a workflow that automates data discovery, profiling and cleansing on a platform designed to compress 6-month data engagements into 3 months.
Sunlighten, the internal SaaS behind residential solar operations: integrations that automate the workflow from sale to installation.
Jackpot Party: deployment-scoped feature-flag and reward configuration on an event-driven architecture.
Multi-tenant ERP SaaS: fiscal and financial back-office routines automated through integrations, without AI. It is the kind of process where AI comes in today.
Facing a similar challenge? Tell us the problem and the timeline.
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AI business process automation is the use of AI and connections between systems to do multi-step work, such as reading documents, checking data and updating your business system, with no manual handoffs. It pays off when a process is high-volume, rule-heavy and already measured. Hire it when such a process takes hours from the team every week.
We map the candidate processes and collect volume, time per case, errors and exceptions. You receive the prioritized list, the baseline of each process and the recommendation, including when it is not to automate.
We define each step: what is a fixed rule, what the model decides, what an agent or subagent executes and where a person approves. You validate the workflow, the permissions, the acceptance criteria and the result metric.
We automate one process on real documents and data, in parallel with the current operation. You receive the comparison with the baseline and a recommendation to proceed, adjust or stop.
Integrations, exception queue, per-run traces and audit trail. The rollout is gradual, by share of the volume. You receive the process in operation, with a metrics dashboard and documentation.
We track the exception rate and the cost per run, adjust the rules and take the pattern to the next process on the list. Operation can continue under a monthly retainer or with your team.
| Scope | Typical timeline | Engagement model |
|---|---|---|
| Process discoveryCandidates, baseline, prioritization by return and risk, plan and estimate | 1–2 weeks | Fixed-scope project |
| Proof of conceptOne process automated on real data, compared with the baseline | 2–4 weeks | Fixed-scope project |
| ProductionProcess automated and integrated with your systems, through a LangGraph workflow or an autonomous agent (OpenClaw, Hermes Agent), with an exception queue, traces and auditing | 6–12 weeks | Fixed scope or time & materials with a cap |
| Operation and new processesMonitoring, rule adjustments and expansion to the next process | Ongoing | Monthly fractional retainer |
Timelines are typical ranges. Discovery defines the scope and the estimate of each project; the contract can be in USD or BRL.
Want to know which format fits your project?
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AI comes in where there is text to interpret and a rule-bound decision; the rest is deterministic code. Every agent, autonomous or not, runs with least privilege, human approval at critical points and a trace.
Where AI comes inDocument reading, classification, field extraction and decisions within written rules; calculations and writes stay in deterministic code.
Human approvalLangGraph workflows stop before amounts above the limit, low-confidence cases and exceptions, and write only after a person approves.
OpenClaw and Hermes AgentBoth run with a closed list of tools and channels, visible scheduled tasks and persistent memory your team can review.
Planning and subagentsThe agent splits the work into a plan, and each subagent gets only the tools and records of its own step, with least privilege.
Evals and tracesReal cases with the expected result run on every change of rule, prompt or model; every run records the input, decision, source, tokens, cost and who approved it.
Your dataClient data never trains models, and we work under NDA.
| Approach | When to use it | Strength | Limit |
|---|---|---|---|
| RPA | Repetitive tasks on screens of systems without an API, with fixed steps | Automates legacy systems without changing them | Breaks when the screen changes; cannot interpret free text |
| Workflow tools (n8n) | Flows between systems that have APIs, with clear rules and triggers | Fast to build and to change; good for integrations | Complex logic and exceptions become hard to test and maintain |
| Agent workflows (LangGraph) | Steps that require reading documents, classifying or deciding with context | Handles free text and pauses for human approval | Needs evaluation, action limits and human review; has a cost per run |
| Autonomous AI agents (OpenClaw, Hermes Agent) | Recurring tasks that span several channels, on a schedule or on a message | Acts on its own across channels and keeps memory between runs | Needs narrow permissions, approval for critical actions and a log of everything |
| A combination | End-to-end processes with fixed steps and interpretation steps | Each step uses the simplest technique that solves it | Requires a single architecture and monitoring layer across all tools |
Start with processes that are high-volume, rule-heavy and already measured: order entry, document checks, record creation, reconciliations, request triage. Their return is the easiest to prove and they carry the least risk. Rare, unstable or ownerless processes come later. Discovery ranks the candidates by volume, time spent, error rate and risk.
By comparing what the process costs today with what it costs once automated. Before the project we measure the baseline: monthly volume, time per case, error rate and rework. Then we compare it with the automated process, adding the project cost, model usage per run and maintenance. Payback is the project cost divided by the net monthly saving. We promise no percentages before measuring your process.
RPA repeats fixed steps on screens; n8n connects systems that have APIs; agents come in where someone has to read text, classify or decide with context. OpenClaw and Hermes Agent fit recurring tasks that span several channels. A real process usually combines these techniques, and for each step we use the simplest one that solves it. Putting AI where a fixed rule is enough only adds cost and risk.
They can be, when they run with least privilege, human approval for critical actions and a log of every step; that is how we set up any agent. Each one reaches only the tools, channels and records of its own process, with credentials in a vault. Scheduled tasks and persistent memory stay visible to your team. Personal data is masked before the model where the use case allows, client data never trains models and we work under NDA. Provider and processing region are chosen with your team in discovery.
You choose: your team, with the documentation and the handover done at delivery, or Master Timm under a monthly retainer. Every automation needs maintenance, because systems, rules and models change. That is why delivery includes monitoring of the exception rate and the cost per run, tests that run on every change and alerts when an integration fails.
Then that is what the report says, with the numbers that led to the conclusion. It happens when volume is low, when the process still changes a lot or when a tool you already have solves it. If that is already clear in the first conversation, we tell you before you hire the discovery. When the conclusion comes out of discovery, you keep the measured baseline and recommendations to simplify the process, the step before any automation.
It depends on the number of systems to integrate, the variety of documents, the volume of exceptions and the level of control required. That is why we do not publish a price list. A 1–2 week discovery delivers the scope and the estimate, and the proposal can be fixed scope or time & materials with a cap, in USD or BRL. In operation there is the model cost per run, which we forecast from volume and monitor.
The workflow is designed on the assumption that the AI will make mistakes. Low-confidence cases and amounts above the limit stop in a human review queue. Deterministic validations check what the model extracted against master data and rules, and every run produces a trace for audit and correction. Each error found becomes a new case in the evaluation dataset.
AI-focused IT consultancy
Senior engineering from discovery to delivery: whoever designs the architecture also writes and reviews the code.
Master Timm · Updated on
In the first conversation we look at two or three candidate processes, their volume and time spent, and tell you which one is likely to pay back first, or whether none is worth it yet.
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