For operationally complex businesses · 15 to 100 employees
Make smarter AI and automation investments.
Identify operational problems with real financial upside, quantify the opportunity, and get an evidence-based recommendation on what's worth building first.
Operator-led in Vermont, with specialist expertise brought in when the work requires it.
The AI & automation audit
A business case before a build.
The expensive problems usually are not labeled “AI problems.” They show up as slow quotes, duplicate entry, missed handoffs, scheduling chaos, document chasing, and owners routing routine decisions.
Illustrative audit output
Decision snapshot
Evidence we would establish
Requests arrive through email, calls, and photos; estimators chase missing details before pricing.
Illustrative value mechanism
Reduce information chasing and re-entry so qualified requests reach an estimator sooner.
Full-cost consideration
Intake changes, estimating rules, system connection, training, and upkeep.
What would change the verdict
Medium confidence until request volume, touch time, response time, and close-rate relationship are baselined.
Human-review point
Estimator approves scope assumptions, exceptions, and final price.
A real recommendation requires your business's evidence, not a generic template.
Quote turnaround selected. Illustrative verdict: build a bounded pilot with estimator review.
- 01
Observe
Costly workflow
Establish the current state: time, delay, errors, rework, or revenue leakage already present in the operation.
- 02
Quantify
Financial case
Model low, base, and high value scenarios alongside full cost, confidence, risks, and what must be true to capture value.
- 03
Decide
Recommendation
Make a build, defer, or do-not-build decision. If it is build, define the workflow, guardrails, human review, and measurement.
Workflow friction map
Look where operational work is expensive and unglamorous.
These are illustrative workflows to investigate, not promises that every workflow should be automated.
Examples include construction, landscaping, HVAC, plumbing, electrical, and property services.
Request received
- What we observe
- Channels, formats, required fields, and who first touches the request.
- Why it may work this way
- Customers use the channel that is easiest for them.
- Business consequence
- Work begins with sorting rather than estimating.
- Evidence needed
- Sample requests, intake fields, arrival time, first-touch time.
- Human judgment / guardrail
- Never reject a request automatically; flag missing information.
Field service: Request received selected. Work begins with sorting rather than estimating.
How it works
Diagnose, decide, then design and build.
Start with how the work runs, what the problem costs, and how the business would capture the value. The technology decision follows from that evidence.
- 01
Diagnose
Watch how people actually do the work. Map handoffs, workarounds, judgment calls, systems, and constraints before changing anything.
- 02
Decide
Model potential and full cost, test assumptions, score confidence and risk, and recommend build, defer, or do not build.
- 03
Design, build, and measure
Define the target workflow, automation boundary, guardrails, exception paths, human review, baseline, and measurement together.
What the audit accounts for
A useful recommendation has to survive the real business.
We look beyond whether technology can perform a task. The recommendation also has to make financial sense, fit the way your team works, use reliable data, manage risk, and produce a measurable result.
- 01
Business value and ownership
What is the problem worth, what would have to change, and who will own the result?
- 02
People and workflow
How is the work actually done, where is human judgment necessary, and what must change for the solution to be used?
- 03
Data and systems
Is the required information accurate, accessible, and connected—or does the foundation need work first?
- 04
Risk and control
What access should the technology have, what can go wrong, and where are approvals, security controls, exceptions, or expert review required?
- 05
Measurement and iteration
What baseline, acceptance criteria, and ongoing measures will show whether the result is real?
Material security, legal, or regulatory requirements are identified during the audit. Deep cybersecurity, compliance, or controls assurance is separately scoped with qualified specialists.
From decision to delivery
If the evidence supports a build, ThrivonAI can lead it.
You do not have to take a report and find someone else to make it real. ThrivonAI remains accountable for the target workflow, solution design, implementation plan, guardrails, acceptance criteria, and measurement.
- 01
Design and scope
Translate the findings into a target workflow, solution architecture, system boundaries, guardrails, acceptance criteria, and implementation plan.
- 02
Build and integrate
ThrivonAI builds directly where the work fits our expertise and brings in and manages the right specialists when deeper platform expertise is required.
- 03
Launch and measure
Test with the people doing the work, train the team, monitor exceptions, and compare the result with the baseline before expanding.
If you already have an internal technology team or implementation partner, ThrivonAI can work alongside them or hand over an implementation-ready plan.
Technical capability
Microsoft specialization. Stack-agnostic recommendations.
Our strongest platform concentration is Microsoft cloud, automation, and AI tooling. We also build around the operational systems already in use and design connected solutions on a shared foundation, so future automations and agents can reuse the same data, permissions, controls, and monitoring.
Core specialization
Microsoft cloud, automation, and AI
Hands-on work across the Microsoft environment, including integrations, workflow automation, hosted services, and agent pilots.
- Microsoft Graph API and SharePoint
- Custom connectors for documents, lists, calendars, and operational data.
- Power Automate and Teams
- Workflow, notification, and operational handoff flows inside Microsoft environments.
- Azure-hosted integration services
- FastAPI services, Key Vault, storage, and supporting cloud infrastructure.
- Copilot Studio and Microsoft Foundry
- Hands-on pilot integrations and provisioned infrastructure, without presenting pilots as completed production deployments.
Live operating systems
Integrations against real business data
Experience operating the systems and then building the connective tissue around the work they support.
- SOS Inventory
- Custom read/write integration with confirmation gates and write verification.
- Faire, SOS, and QuickBooks workflows
- Wholesale order processing across channel, inventory, fulfillment, and financial data.
- Klaviyo connections
- Connections for working with real campaign and segmentation data.
- Shopify, QuickBooks Online, and HighLevel
- Firsthand operating experience across commerce, finance, and CRM workflows.
Build capability
Connected solutions and agent foundations
Each build is designed around shared data, permissions, observability, and controls so future automations and agents can extend the same foundation instead of becoming isolated tools.
- MCP and reusable system connections
- Shared connections let approved solutions and agents work with the same business systems instead of duplicating integrations.
- Permissions and human review
- Access boundaries, confirmation before consequential actions, and named review points are designed into the workflow.
- Observability and exception handling
- Visible failures, verified writes, logs, and exception routing make operational behavior easier to monitor and improve.
- Specialist delivery model
- Additional platform expertise can be sourced and managed when the implementation requires it.
Microsoft is a specialization, not a requirement. ThrivonAI can design and manage implementation, work with your existing team, or bring in deeper platform specialists where the build requires them.
Who is behind ThrivonAI
Operational experience before AI advice.
ThrivonAI was founded by Kent Arnold. The company remains operator-led, with specialist expertise brought into audits and builds when the work requires it.
- 01
Co-founded and ran operations at a food manufacturing business for six years
- 02
Worked across manufacturing, inventory, wholesale, distribution, DTC, and events
- 03
Built operational systems and automations against live business workflows
- 04
Founded ThrivonAI to apply that operator lens to AI and automation investment decisions
ThrivonAI is built for operationally complex small and midsize businesses with 15–100 people, particularly field service, food/CPG, distribution, and other operationally heavy companies.
Questions SMB leaders ask before spending on AI
How do I decide what AI or automation to build first?
Start with the operation, not the tool. ThrivonAI identifies where work is being re-entered, delayed, checked, chased, or trapped with one person; establishes a baseline; models the financial upside and full cost; then recommends build, defer, or do not build. When the answer is build, redesigning the workflow is part of designing the solution.
What if we cannot afford an in-house AI person?
Most 15-to-100-person businesses do not need to hire a full-time AI executive before they know what is worth doing. A bounded AI and automation audit gives leadership a prioritized decision, evidence, costs, risks, and next steps without adding permanent overhead.
Does the answer always involve AI?
No. The right answer may be AI, conventional automation, an integration, a process change, better data, or leaving the workflow alone. The technology is selected after the problem and value case are understood.
Which businesses are a good fit?
ThrivonAI works best with businesses of roughly 15 to 100 people where operational complexity is creating costly manual work and a decision-maker can act. Strong fits include construction, landscaping, field and home services, food manufacturing and CPG, distribution, and other operationally heavy businesses.
How can I evaluate someone selling an AI solution?
Ask them to show the current-state baseline, the source of every assumption, low/base/high value scenarios, the full implementation and operating cost, risks, a measurement plan, and the conditions under which they would recommend not building. Be cautious if the solution was chosen before the workflow was examined.
Do you build the recommended solution?
When the evidence supports a build, ThrivonAI can scope and implement the work in milestones or bring in the right delivery partner for the stack. The build includes designing the target workflow, deciding what technology should handle, preserving the right human judgment, and adding the required guardrails and review points.
A low-commitment starting point
Start with what feels slow, messy, costly, or too dependent on one person.
No preparation required. Use the 30 minutes to describe how the work runs. The goal is a clearer view of what may be worth examining and whether a deeper audit makes sense.