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Solution

Intelligent Automation.

RPA and AI hybrid workflows engineered for production: with audit trails, human-in-the-loop checkpoints, and the operating discipline regulated environments require. Not a UiPath demo with a victory lap.

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Workflow latency
−40 to −70%
Manual touch reduction
−50 to −85%
Error rate post-automation
−60%+
Time to first prod automation
≤ 4 mo

The condition

Most RPA programs ship a hundred bots that nobody can govern.

The pattern across enterprise RPA programs: a citizen-developer push that produces 200 bots in eighteen months; an operations team that inherited the bots without a charter to refactor them; a governance frame that's a SharePoint site; AI features bolted on without an eval discipline; an outage when the underlying SaaS UI changes; and a CFO asking 'what was the ROI on the $4M we spent'. Automation succeeds when the engineering, governance, and AI eval discipline are all primary scope.

Prosigns ships intelligent automation as a governed engineering practice rather than as a citizen-developer free-for-all. We design the automation portfolio with explicit ROI hypotheses, build under a documented governance frame, integrate AI use cases (document understanding, agent orchestration, decision support) with the eval discipline production AI requires, and deliver the audit trail that regulated workflows actually need. CORTEX handles the AI; FORGE handles the orchestration; CITADEL handles the audit frame.

What success looks like

Outcomes you can track monthly.

Every intelligent automation engagement publishes a metrics dashboard at kickoff and updates it monthly. No vanity metrics, no mystery ROI.

Workflow latency
−40 to −70%
Manual touch reduction
−50 to −85%
Error rate post-automation
−60%+
Time to first prod automation
≤ 4 mo

Practice mix

4 practices, orchestrated against one outcome.

Solutions are not single-practice engagements. The roles below show how each practice contributes, the same way a delivery plan names owners and acceptance criteria.

  1. 01

    CORTEX

    AI & Machine Learning

    Generative AI, agents, computer vision, predictive analytics, and MLOps, engineered for production.

    Role here

    AI components in the workflow: document understanding, classification, agent orchestration with HITL checkpoints, and the eval harness production AI requires.

    Open the practice
  2. 02

    FORGE

    Custom Software

    SaaS, enterprise applications, legacy modernization, integrations, and mobile.

    Role here

    Workflow orchestration, integration with enterprise systems, and the operational tooling automation operations actually use day-to-day.

    Open the practice
  3. 03

    ATLAS

    Enterprise Platforms

    Implementation, customization, and managed support for Dynamics 365, Salesforce, Power BI, ServiceNow, Shopify Plus, and ERPNext.

    Role here

    Power Automate, ServiceNow Workflow, Salesforce Flow. Implemented with extension boundaries and continuous-upgrade discipline.

    Open the practice
  4. 04

    GUARDIAN + CITADEL

    Quality & Security

    Test automation, performance, accessibility, application security, secure SDLC.

    Role here

    Audit-trail tooling, compliance evidence collection, and the AppSec discipline regulated automation requires.

    Open the practice

How we engage

4 phases, orchestrated across practices.

Each phase has named owners across the practices listed above, a shared deliverable, and an acceptance criterion at the program (not the squad) level.

  1. 01

    Portfolio prioritization with ROI hypotheses

    Discovery scores candidate workflows on volume, value, complexity, and feasibility. Each automation candidate gets an explicit ROI hypothesis the program tracks against. We tell you which workflows shouldn't be automated.

  2. 02

    Governance frame first

    Documented automation governance before the first bot ships: ownership, change-management, security review, monitoring, decommission criteria. Citizen-developer surfaces gated by the governance frame, not the other way around.

  3. 03

    AI with eval discipline

    AI components (document understanding, classification, agentic workflows) ship with eval datasets, faithfulness scoring, refusal patterns, and audit logging. Not RPA + LLM duct tape, engineered AI in production workflows.

  4. 04

    Operate and decommission

    Quarterly portfolio review: which automations are paying ROI, which need refactor, which should be retired. Most enterprise programs find 15–30% of bots have outlived their workload. We help retire them rather than maintaining them defensively.

Capabilities

What’s in scope.

Capabilities span all the practices contributing to this solution. Out-of-scope items are named in the SOW too.

  • Automation portfolio prioritization with ROI hypotheses per candidate
  • Documented governance frame: ownership, change, security, monitoring
  • RPA orchestration: UiPath, Automation Anywhere, Blue Prism, Power Automate
  • AI in workflows: document understanding, classification, agent orchestration
  • Workflow orchestration: ServiceNow Flow, Salesforce Flow, custom
  • Eval discipline for AI components: ground-truth datasets, scoring, drift
  • Audit logging that satisfies SOX, HIPAA, FFIEC, and regulator inquiry
  • Decommission discipline: structured retirement of automations that age out

Industries

Where intelligent automation lands.

Most-frequent buyer industries. Each card opens the industry-scoped playbook with sector-specific compliance and operating constraints.

Selected work

Quantified outcomes, not adjectives.

All case studies

Common questions

Asked before the first call.

  1. 01

    Is this RPA or AI?

    Both, with discipline. RPA is the right tool for stable, structured-data workflows that interface with legacy systems through UI. AI is the right tool for unstructured input, judgment, and language. Most production workflows mix both; we design the boundary explicitly per workflow rather than defaulting to whichever is trendy.

  2. 02

    Do we need a CoE (center of excellence)?

    Not always. CoE structures work when the customer has 50+ active automations and dedicated automation operations staff. Smaller portfolios are better served by an embedded engineering practice with explicit governance handed back to existing engineering or operations. We tell you which structure fits.

  3. 03

    How do you handle UI-fragility in RPA?

    We avoid UI automation when API alternatives exist. Where UI automation is unavoidable (legacy systems with no API), we engineer for fragility: explicit selectors, automated regression tests, monitoring on bot health, and the response runbook for vendor UI changes. We tell you when an RPA candidate is fundamentally fragile and recommend alternatives.

  4. 04

    What about citizen developers?

    Supported, with governance. Citizen-developer surfaces (Power Automate, App Engine Studio) gated by the governance frame: review, security, change-management. We design the operating model that lets citizen developers ship without producing the bot sprawl most programs experience.

  5. 05

    Can you migrate off UiPath / Automation Anywhere?

    Yes: vendor migrations are increasingly common as license economics shift. We design migrations workload-by-workload (lift, replatform, retire) with explicit ROI analysis. Many migrations conclude with 'retire 30%, replatform 50% to Power Automate, replatform 20% to custom orchestration'.

  6. 06

    What does an intelligent automation engagement cost?

    Portfolio assessment: 4–6 weeks, $50K–$150K. First production wave (3–8 automations): 4–8 months, $400K–$1.2M. Multi-quarter automation programs: $1M–$4M+ depending on scale. Managed Services for automation operations: $40K–$200K monthly retainer.

Talk to us

Bring an intelligent automation mandate. We’ll bring a senior engineer.

A senior engineer plus the practice leads who’d staff this program join the first call. No discovery gauntlet, no junior reps.

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