Start a Conversation
Home > About > FAQ

― FAQ

FAQ

Who is Prosable Outcomes?

Prosable Outcomes designs, builds, and operates AI-enabled business processes. We close the gap between AI capability and reliable production operations, building the exception management, continuous improvement, and governance structure that keeps workflows performing as they scale.
Running business processes where AI agents handle the routine volume and human specialists handle the judgment calls, with routing, exception handling, and a learning cadence around the whole system. The agent does the work that fits the rules. People handle the work that requires context, interpretation, or a decision that has not been made before.
Traditional BPOs add labor to existing processes. We restructure the process around AI and tiered human roles (L1 AI workers for routine volume, L2 specialists for judgment, L3 domain experts for the hardest cases) and then systematically reduce the manual load through continuous learning cycles. The goal is an operation that needs fewer people on fewer exceptions, not a bigger team doing the same work.
Most AI consulting firms are structured to build. The incentives, the engagement model, the deliverables are all oriented around shipping a workflow, not running one. What the operation looks like under production load, six months in with exceptions accumulating and edge cases multiplying, rarely shapes the design from the start. Prosable works differently. We design for the operation first, then build toward it. The result is not a delivered project. It is a durable operation.

Most enterprise technology follows a predictable pattern: assess, plan, build, deploy. That works when cause and effect are analyzable and best practices exist. ERP implementations, CRM rollouts, and traditional automation projects can be managed as finite projects because the right expertise can design the right solution before production begins.

Operations powered by AI agents work differently. In production, workflows generate exceptions that were not anticipated during design, and the variety grows as volume and context shift. Edge cases multiply. Business context changes. The operation has to adapt continuously, not execute a fixed plan. Dave Snowden’s Cynefin framework describes this as the difference between complicated systems (where analysis and best practices work) and complex systems (where patterns only emerge through operation and response). Prosable’s operating model is built for that complexity: weekly exception reviews surface what production is revealing, monthly playbook updates codify what the team has learned, and quarterly process redesign improves the system structurally. The design is the starting point, not the answer. The answer emerges from running the operation.

Platforms are getting better at resolving routine exceptions through automated reasoning, pattern matching, and retry logic. For known patterns at scale, they are fast and consistent. The challenge is that the exceptions with the most operational impact are rarely the routine ones.

The cases that matter most require business judgment the platform does not have: a customer dispute where the contract says one thing and the sales commitment says another, a compliance edge case where the regulation is ambiguous and the risk tolerance depends on context, a routing decision where two departments have conflicting priorities and someone has to decide whose workflow wins. These are not data problems. They are judgment problems, and they surface continuously as business context shifts.

Platforms also face a structural limitation in how they learn. A platform can detect that a pattern of exceptions is recurring. It cannot decide whether that pattern means the playbook should change, the process should be redesigned, or the exception is acceptable and should continue to be handled manually. That decision requires understanding what the business is trying to accomplish, not just what the data shows. Prosable builds on these platforms, not around them. We provide the operating layer where those judgment calls get made, codified, and fed back into the platform so it handles more with each cycle.

Prosable is a technology-powered services firm. Discovery is tool-led: software-based analysis produces evidence-based findings faster than traditional consulting. Delivery combines AI agents handling volume with human specialists handling judgment. Pricing is tied to results, not hours. This model is different from traditional consulting, where discovery and delivery are both human-led, and different from software products, where the human layer is removed entirely. Prosable keeps the human judgment where it matters and uses technology to drive the speed, consistency, and learning cycles that compound as the operation matures.

Operations that measurably improve. Exception rates that decline. Resolution times that shorten. Manual intervention that reduces as the system learns. Operating costs that decrease as automation handles more of the volume and humans focus on the work that requires judgment. Every engagement is priced against these operating outcomes. The risk is shared: if the operation does not perform, the pricing model reflects it.

Organizations that engage Prosable typically fall into one of four situations:

  • Deployed and hitting the exception wall. The workflow is in production and handling what it was built for. Whatever it cannot handle gets absorbed however it can, with no routing, no clear owner, and no visibility into what it costs.
  • Regulated industries facing oversight mandates. The EU AI Act, Colorado AI Act, and emerging state-level requirements have moved AI governance from future planning to active compliance deadlines. The workflow has been live for months, but the documentation, decision logs, and accountability records the regulation asks for were never built.
  • Failed or stalled deployments needing triage. An AI initiative stalled before reaching production, or launched and failed shortly after. Before the next budget cycle commits more resources, the organization needs a clear account of what broke and whether rebuilding makes sense.
  • Early programs where the goal is to build it right from the start. The first production workflow is on the roadmap. Leadership wants the operating model, exception handling, and governance designed in from the start because they have seen what it costs to retrofit them after the first production failure.

In every case, the pattern is the same. The technology is not the problem. The operating layer around it is.

Three engagement types, each with defined scope and deliverables. Assessments and diagnostics find the right starting point and identify where to focus first. The Prosable Path takes a workflow from design through operating proof in staged phases. Prosable Operations runs production workflows, managing exceptions and improving performance over time. Each type ends with something concrete: a scored evaluation and prioritized starting point, a workflow running in production with the operating model built in, or a managed operation with performance measured against committed targets.

Engagements are priced against committed outcomes, not hours. Assessments and diagnostics are fixed-price with defined deliverables. The Prosable Path is priced by phase, with a defined output at each stage. Prosable Operations uses hybrid pricing: a base retainer plus per-resolution fees for managed models, or monthly fees that step down for embedded models as the internal team takes on more.

Yes. We build agents, configure platforms, and handle integration work as part of the Prosable Path. The Build phase covers agent configuration, data connections, routing logic, and the exception handling framework. The distinction is that the workflow and the operating model get designed together, not sequentially. We also work with agents built by the client’s internal team, by vendors, or by implementation partners when the technology layer is already in place.

Microsoft Copilot Studio, Salesforce Agentforce, ServiceNow AI Agent Studio, Databricks Agent Bricks, and AWS Bedrock AgentCore, among others depending on the client’s environment. AI models come from OpenAI, Anthropic, Google, and Microsoft, selected by use case. Platform choice follows operating requirements and what the client already has in place, not vendor relationships.
Tool-led discovery instead of interview-led consulting. Engagements use tools like Nudge Security for SaaS and shadow AI visibility, Microsoft Viva Insights and Viva Pulse for organizational analytics, Mimica for process intelligence, and Databricks Genie for data analysis. These produce consistent, evidence-based findings in a fraction of traditional consulting timelines. The team interprets the results, identifies what matters, and turns findings into a prioritized path forward. Tool selection is based on what the engagement requires, not vendor partnerships.
The AI Readiness Assessment determines whether the organization can support what it is trying to do with AI, and where to focus first. It evaluates eleven capabilities across leadership alignment, process maturity, governance, and technology. The output is a scored evaluation with a prioritized roadmap: where there is momentum to build on, where gaps will create drag, and what to address first.
No. There are several ways to narrow scope. If the team already knows which operating area to start with, a Focused Assessment (2-3 weeks) evaluates all eleven capabilities for a single business unit. If individual AI adoption is already happening but usage is not yet coordinated and leadership needs to align on where to start, the AI Operating Baseline (2-3 weeks) uses discovery tools and targeted conversations to find the right starting point without scoring every capability. And for organizations that do not know where to begin at all, a few targeted questions can reveal where conditions are strongest: What does success look like in 12 months, and does leadership share that definition? How many change initiatives are already competing for attention? When something falls outside the standard path in a process today, what happens? Those questions cut across departments and point to which area has the best conditions to go first.
Technology is usually not what holds things up. More often, individual adoption has already outpaced the organization. Someone on the finance team is already writing policies with Copilot. Use case ideas are coming in from multiple departments. But there is no shared view of what success looks like, no guardrails in place, and no way to prioritize which ideas to pursue first. Other common findings: the team cannot articulate how its process actually works today (including the workarounds), and it is unclear who has authority to approve exceptions or change the process. The organization cannot measure what a process costs, so there is no baseline for improvement, and people are hesitant to move because they are afraid of getting it wrong. These are the things that stall an initiative after launch, and they are easier to address before the build starts.
A free 45 to 60 minute conversation. Two modes: exploration, for leaders who see an opportunity but have not scoped it yet; and diagnostic, for teams with a specific workflow or operating issue in mind. The session ends with a recommendation on where to start and why. From there, engagements typically move into the AI Readiness Assessment or the AI Operating Baseline, depending on scope. Prosable follows up within two business days.
The technology is usually not what breaks. What determines whether an operation lasts is the structure built around it: how exceptions get routed and resolved, how escalation paths work, who owns what, and how the operation improves based on what production keeps revealing. A workflow without that structure performs in the demo and degrades under load. Building that structure, from the first workflow through production scale, is what Prosable is built to do.