Process mapping and audit
We sit with the people who do the work and document each step, exception, and workaround. The map tells us what to automate first and what to leave alone.
006Work Automation
Work automation that gives your team its hours back
We build software that takes over the repetitive work your team does by hand: data entry, approvals, handoffs between systems, report assembly, and the follow-ups nobody enjoys. It is for operations leaders and CTOs whose people spend more time moving information around than acting on it. We map the process first, then automate the parts a machine should own.
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What we build
What work automation covers
Every engagement is scoped around your actual processes, but most automation work falls into a few recurring categories.
Workflow engines
Multi-step processes with rules, branching, and human approval points, built as software your team can see and adjust. Requests move forward on their own instead of waiting in someone's inbox.
System integrations
We connect your CRM, ERP, accounting, and internal tools through their APIs so data entered once shows up everywhere it is needed. No more re-keying between systems.
Document processing
Invoices, forms, contracts, and scans read by OCR and extraction models, validated against your rules, and pushed into the right system. Humans review only the cases the software flags.
Scheduled jobs and reporting
Recurring reports, reconciliations, syncs, and reminders that run on a schedule and alert someone only when a number is off. The Monday-morning spreadsheet builds itself.
Monitoring and exception handling
Every automation we ship logs what it did, retries sensible failures, and routes real exceptions to a person with context. You always know what ran and what needs a human.
How we work
From manual process to running automation
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Audit the process
We observe how the work actually happens, not how the procedure document says it happens. The output is a prioritized list of automation candidates with effort and payoff estimates.
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Design the workflow
We specify each automation end to end: triggers, rules, integrations, exception paths, and where a human stays in the loop. You approve the design before we write code.
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Build and integrate
Senior engineers build the automation against your real systems and data, in short iterations you can inspect. We test the exceptions as hard as the happy path.
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Roll out and refine
We run the automation alongside the manual process until you trust it, then cut over. After launch we watch the logs, tune the rules, and pick up the next process on the list.
Why Webisoft
Why teams automate with us
Automation projects fail when they are treated as scripting exercises. We treat them as production software.
Senior engineers only
The people who scope your automation are the people who build it. No handoff to a junior bench after the kickoff call.
Built on your stack
We work with the systems you already run, whether that is a modern SaaS suite or a legacy ERP with an awkward API. We do not force a platform migration to get value.
Humans stay in control
We automate the mechanical steps and keep judgment calls with your team. Approvals, overrides, and audit trails are designed in from day one.
Code you own
Everything we build is documented, tested, and handed over with the source. Your team can run it, extend it, or hire anyone else to, without asking us for permission.
FAQ
Work automation questions, answered
(4)- Often the answer is both. Workflow platforms like Zapier or Make handle simple, well-supported cases quickly and cheaply. Custom automation earns its keep when a process has real business logic, high transaction volume, or systems that off-the-shelf connectors cannot reach. A practical approach is to start with a packaged tool where it clearly fits and move to custom software once the process outgrows it or the tool's limits start creating workarounds.
- A single well-defined workflow usually ships in a few weeks. Larger programs that touch several departments and systems run in phases over several months. The most reliable projects deliver a working automation at the end of each phase rather than a long design document, so the business sees value early and can adjust scope as it learns.
- Yes, and legacy systems are one of the most common reasons companies invest in automation. If a system exposes an API, automations integrate with it directly. If it does not, integration typically works through the system's database, scheduled file exports, or its interface layer. An old system is usually a driver for automation rather than a blocker, because it is exactly where the most manual work accumulates.
- Well-designed automations separate rules and configuration from code, so routine changes such as new thresholds, approvers, or categories are settings updates rather than rewrites. Bigger process shifts require development work, which is why documentation, tests, and clear code ownership matter at handoff. Teams that own their automation code, or keep a support arrangement in place, can adapt to change without starting over.
Automation capabilities
Where automation actually pays for itself
The best automation targets are repetitive, rule-heavy, and error-prone tasks that eat skilled people's time. We build automations that survive contact with real data, real exceptions, and real staff turnover.
Workflow orchestration
Multi-step business processes wired together with tools like n8n, Temporal, or custom services, so approvals, notifications, and handoffs happen without someone chasing them. Every workflow includes explicit failure paths, because an automation that fails silently is worse than the manual process it replaced.Document and data extraction
Invoices, contracts, and forms parsed with OCR and LLM-based extraction, validated against business rules before anything enters your systems. We measure extraction accuracy on your real documents during a pilot, and route low-confidence cases to a human review queue instead of guessing.System-to-system integration
Automated data flow between your CRM, ERP, accounting, and internal tools through their APIs, replacing the export-edit-import ritual and the copy-paste errors that come with it. Where a legacy system has no API, we weigh screen automation against a database-level integration and are honest about the fragility of each.Human-in-the-loop design
For decisions with financial or legal consequences, the automation prepares the work and a person approves it, with the interface showing exactly what will happen on approval. This pattern gets most of the time savings while keeping accountability where regulators and auditors expect it.AI agents for judgment tasks
Where a task needs reading and light judgment, triaging support tickets, drafting responses, categorizing records, we build LLM-backed agents with guardrails, logging, and an evaluation set so quality is tracked over time. We say plainly when a deterministic script is the better tool, because it often is.Monitoring and exception handling
Dashboards showing what ran, what failed, and what is stuck in queue, with alerts routed to the people who own the process rather than to a shared inbox nobody reads. This operational layer is what separates automation that lasts years from a script that quietly dies after the first API change.
How we automate
From process mapping to automation your team trusts
(4)- 1
Process mapping and ROI check
We sit with the people who do the work and document the real process, including the exceptions and workarounds that never appear in the official procedure. Each candidate process gets a simple ROI estimate, hours saved against build and maintenance cost, and weak candidates are cut before anything is built. - 2
Pilot on real data
The first build runs on live data for a limited scope, one document type, one team, one region, alongside the manual process rather than replacing it. This surfaces the edge cases that decide whether the automation is viable, and gives you measured accuracy numbers instead of promises. - 3
Hardening and rollout
Once the pilot holds up, we add the unglamorous parts that make automation dependable: retries, idempotency, audit logs, permission checks, and a clear procedure for when a human needs to intervene. Rollout expands scope step by step with the affected teams trained at each stage. - 4
Handover and measurement
Your team gets documentation, admin access, and training to operate and adjust the automation without us. We review the actual hours saved and error rates against the original estimate after a full cycle of use, because an automation that cannot show its numbers should not survive budget review.
FAQ
Common questions about workflow automation
(6)- The strongest candidates are high-volume, rule-based tasks with digital inputs: invoice processing, data entry between systems, report generation, onboarding checklists, and routine customer notifications. Poor candidates are processes that change every month, depend heavily on judgment, or run so rarely that the build cost never pays back. A practical filter is to automate a process only after it is stable and well understood manually, since automating chaos just produces faster chaos.
- The basic model is hours currently spent times loaded labor cost, compared against build cost plus ongoing maintenance, which is commonly 10 to 20 percent of the build per year. Error reduction should be counted too, since a mistyped invoice or missed compliance step often costs more than the labor. Be skeptical of ROI claims that assume 100 percent of a task disappears, because exceptions and review work usually keep 10 to 30 percent of the original effort in place.
- RPA drives existing user interfaces by simulating clicks, which needs no API but breaks when screens change. Workflow automation connects systems through their APIs and is far more stable, but requires those APIs to exist. AI agents add language understanding for tasks like reading documents or drafting replies, at the cost of probabilistic output that needs guardrails and review. Most real deployments combine them: APIs where possible, RPA for legacy gaps, AI only where judgment is genuinely required.
- Well-built automation treats exceptions as a designed path, not an afterthought: unclear cases route to a human queue with context attached, failures retry safely without duplicating actions, and every run is logged for audit. The dangerous pattern is an automation that guesses when uncertain or fails silently, because errors then accumulate unnoticed in downstream data. A good acceptance test is asking to see the exception queue and failure dashboard before signing off, since their absence predicts trouble.
- Automations hold credentials to multiple systems, so they concentrate access that used to be spread across people, and those credentials must live in a secrets manager with least-privilege scopes, not in scripts. Data flowing between systems may cross regulatory boundaries, which matters under GDPR, HIPAA, or Quebec's Law 25 depending on where customers are. Audit logging is the other essential: every automated action should be attributable, timestamped, and reviewable, or the automation becomes a compliance blind spot.
- Done well, barely: the standard pattern runs the automation in parallel with the manual process for a few weeks, compares outputs, and only then shifts volume over. Disruption comes mostly from skipping that overlap or from failing to involve the people who own the process, who know the exceptions and can veto adoption in practice if ignored. Roles usually shift rather than vanish, with staff moving from doing the task to reviewing exceptions and improving the process, and communicating that early makes adoption far smoother.
