FOUNDER-LED AI CONSULTING
Evaluate AI workflows and agents, understand their economics, and build agentic tools with clear scope and measurable outcomes.
Choose an evaluation sprint, a spend review, or a scoped implementation. Start with the business question that matters most.
AI EVALUATION & ASSURANCE
Independently assess AI applications and agents against real business workflows. Understand where they succeed, where they fail, and what needs to change before wider adoption.
For teams piloting an AI application, preparing a production release, or reviewing an existing system. We agree on the business task, expected outcome, and acceptable risk before deciding how to measure performance.
AI CONSULTING
From internal transformation to customer-facing products, we connect AI opportunities to practical delivery and measurable business value.
For enterprise teams launching an AI initiative, improving an existing workflow, or bringing an AI product to market. We connect business priorities, technical delivery, and adoption through a scoped plan with clear ownership, practical milestones, and measurable outcomes.
AI AGENTS & AGENTIC TOOLS
Build agents that retrieve information, use approved tools, and carry out defined steps in your business workflows. Start with the business requirement, then decide where an agent adds value and where a simpler automation is enough.
Agree on outcomes, permitted actions, data access, and approval points for research, service operations, planning, or product workflows.
Implement tool integrations, retrieval, routing, and orchestration. Define permissions, handoffs, and failure handling for the selected workflow.
Evaluate task completion, tool selection, out-of-scope actions, unexpected inputs, and escalation with business subject-matter experts.
Monitor agent runs, costs, retries, approvals, and outcomes. Retain execution records, set stopping conditions, and hand over operational ownership.
You receive: an agreed agent scope, tool and integration design, a working pilot, evaluation results, cost attribution, and a scoped production and handover plan. Deliverables depend on the engagement.
AI SPEND & ECONOMICS REVIEW · TWO WEEKS
For enterprise teams, software companies, and PE-backed businesses already using AI. Understand cost drivers, examine unit economics, and decide which improvements deserve investment.
Assess the cost to build and run AI features, including infrastructure and human review, and compare costs with expected or measured ROI. Senior-led analysis informed by Big Four consulting experience, with finance and engineering working from the same evidence. Start with one business unit, up to three AI providers, and two priority workloads.
Reconcile invoices and usage, confirm scope, and identify spending owners.
OutputSpend inventory, reconciled baseline, and data-quality gaps.
Connect costs to models, workloads, teams, and customers or features where the data supports it.
OutputEditable cost-allocation and unit-economics workbook.
Examine model selection and routing, agent loops, tool calls, context size, retries, caching, license usage, and commitments.
OutputEvidence-backed opportunities with estimated benefit, confidence, and effort.
Validate opportunities with finance and engineering against quality, latency, and business requirements.
OutputBusiness case and prioritized implementation backlog with owners.
Review the findings with leadership and agree on the next actions and how to measure them.
OutputExecutive briefing, monitoring recommendations, and a 30/60/90-day action plan.
Clear scope from the start. The two-week timetable begins once agreed inputs and access are available. We identify and estimate opportunities; production changes and verified savings require a separately scoped implementation. Estimates distinguish cash savings, avoided future costs, and potential revenue improvements.
Missing telemetry is documented, not replaced with invented precision. Raw prompts and customer documents are generally unnecessary for the initial review. Scope, fee, and client responsibilities are agreed before work starts.
AI EVALUATION SPRINT · METHODOLOGY & DELIVERABLES
Build evaluation into development and release decisions. Use a repeatable set of business scenarios to check changes to models, prompts, tools, and workflows against the same baseline.
After deployment, define review checkpoints, failure reporting, and human oversight so evaluation continues as usage grows. Ongoing assurance is scoped around your system and available telemetry.
AI EVALUATION SPRINT · 2–4 WEEKS
Start with a defined application or agent and a prioritized set of workflows. The sprint establishes a baseline and produces evidence for remediation, optimization, and release decisions.
Understand the use case, users, workflow, expected outcomes, and risks.
OutputAgreed scope, success measures, and evaluation boundaries.
Develop representative scenarios, edge cases, scoring criteria, and human-review guidance.
OutputBusiness-specific evaluation framework and test dataset.
Evaluate outcomes, accuracy, agent behavior, reliability, cost, latency, and risk.
OutputPerformance scorecard with observed results and evidence gaps.
Group failure patterns and investigate likely causes across prompts, retrieval, tools, and workflow design.
OutputFailure analysis with severity and supporting evidence.
Assess readiness and rank changes by business impact, risk, effort, and expected economic benefit.
OutputProduction-readiness assessment and remediation roadmap.
Where agreed changes and access are available, rerun the baseline scenarios to assess improvements and regressions.
OutputComparison against the original baseline, where included in scope.
Clear scope and evidence. The 2–4 week timetable begins once agreed inputs and access are available. Findings apply to the scenarios and system version assessed. Missing evidence and coverage limits are documented. Implementation, retesting, and ongoing monitoring are agreed explicitly in the engagement scope.
Use the findings to improve the system, validate changes, and establish repeatable release and monitoring practices. Connect quality to cost per successful task, then turn the priorities into an implementation and adoption plan.
EVALUATE → REMEDIATE → VALIDATE → MONITOR
WHO WE SERVE
Prioritize use cases, automate workflows, integrate systems, and drive adoption.
Validate AI features, build and launch capabilities, and manage quality, cost, and monetization.
Assess portfolio opportunities, prioritize AI value creation, and execute improvements with portfolio-company teams.
Build differentiated AI offerings, repeatable delivery models, and stronger client capabilities.
INDUSTRIES & BUSINESS FUNCTIONS
Practical AI applications across industries and business functions—from insight and planning to everyday execution.
AI IN PRACTICE
Prioritize high-value use cases and improve reporting, research, onboarding, and document workflows.
A scoped roadmap, integrated workflows, and an adoption plan.
Validate customer needs, design AI features, and take capabilities from prototype to production.
A tested feature, launch plan, and quality and cost measures.
Identify AI opportunities across portfolio companies and turn priorities into focused delivery initiatives.
An opportunity scorecard, company-level roadmap, and value tracking.
Build differentiated AI services and equip boutique teams with repeatable methods and practical tools.
Offer packaging, delivery playbooks, training, and go-to-market assets.
BUSINESS STRATEGY & MANAGEMENT CONSULTING
Decide where to invest, what to prioritize, and how to execute—with AI supporting your business goals.
FOCUSED REVIEWS AND IMPLEMENTATION
Assess AI applications, agents, and their tool use against real workflows in a focused 2–4 week engagement.
Example deliverablesEvaluation scorecard, failure-mode analysis, economics analysis, and a prioritized remediation roadmap.
Understand model, agent, and tool-call spending and what to change in a focused two-week review.
Example deliverablesSpend baseline, editable unit-economics workbook, opportunity register, and a 30/60/90-day action plan.
Define, prioritize, and deliver AI initiatives—from internal transformation to customer-facing products. Connect business value, technical execution, and commercial outcomes.
Example deliverablesUse-case portfolio, prioritization scorecard, business case, solution architecture, working implementation, evaluation results, deployment runbook, and adoption plan.
Design and implement agents that use approved tools and business systems to complete defined tasks, with explicit action boundaries and human approvals.
Example deliverablesAgent and tool design, system integrations, evaluation evidence, usage and cost tracking, and operational handover.
Automate proposals, reporting, research, onboarding, and knowledge work.
Example deliverablesPrioritized workflow assessment, working automation, and handover.
Build an AI consulting or service practice: offerings, delivery model, tools, governance, and playbooks.
Example deliverablesService portfolio, delivery playbook, and launch roadmap.
Connect AI costs to customers, features, pricing, and margins.
Example deliverablesCost model, pricing scenarios, and margin analysis.
Executive and employee training on ChatGPT, Claude, and Copilot, covering prompting, agents, use cases, and safe adoption.
Example deliverablesRole-specific workshops and adoption playbook.
Define your ideal customer profile, AI offering, packaging, pricing, positioning, sales playbook, demos, and pipeline strategy.
Example deliverablesPositioning, packaging, demo outline, and sales playbook.
Deliverables are scoped to your business and agreed before work begins.
THE SEEBEYOND AI DELIVERY FRAMEWORK
A structured approach to AI delivery, informed by Big Four consulting experience. We connect each stage to clear deliverables, decision gates, and measurable outcomes.
Explore business problems, customer needs, workflows, and potential use cases.
Key outputsProblem statement, customer insights, use-case inventory, and accountable owner.
Decision gateIs there a clear problem and a business owner?
Rank opportunities by customer value, commercial potential, portfolio relevance, feasibility, cost, and risk.
Key outputsPrioritization scorecard, baseline measures, and business case.
Decision gateWhich use case justifies investment?
Prototype and test with users; define architecture, data controls, evaluation criteria, and build-versus-buy decisions.
Key outputsSolution blueprint, evaluation plan, and feasibility evidence.
Decision gateDoes the evidence support building it?
Engineer the production solution, integrate it into products and systems, and test quality, reliability, and failure handling.
Key outputsWorking implementation, integration tests, and acceptance results.
Decision gateDoes it meet the agreed acceptance criteria?
Prepare go-to-market and support, roll out in stages, enable users, and rehearse rollback.
Key outputsDeployment runbook, launch and adoption plan, and operational handover.
Decision gateCan the business operate it reliably?
Track quality, usage, retention, unit economics, and business outcomes; improve, expand, or retire capabilities.
Key outputsPerformance scorecard, improvement backlog, and scale decision.
Decision gateShould we improve, expand, or stop?
Across every stage: security, privacy, governance, human oversight, and change management.
AI READINESS CHECK
Answer six questions for a practical starting point. No email required to see your result. This is a self-assessment, not an audit or a prediction of business performance.
SEE WHAT DELIVERY CAN LOOK LIKE
Preview the structure and level of detail before you book. Includes six worked examples, starting with an AI project charter and use-case prioritization scorecard, followed by a workflow assessment, practice launch plan, AI cost model, training plan, and go-to-market brief.
All examples use fictional businesses and illustrative assumptions. They are not client work or evidence of achieved results.
COMMON QUESTIONS
No. Bring the business problem to an introductory consultation; we can discuss which scope would be useful.
We start by understanding your environment and constraints. Tool choices and integration feasibility are confirmed during scoping.
We agree scope, deliverables, responsibilities, fees, and timing before work begins. The sample pack illustrates the approach, not a fixed contract.
We store the name, email, optional company, request time, contact preference, and any assessment score shared with the request. Access is restricted to SeeBeyond’s system administrators. Submitting the form unlocks samples; it does not verify ownership of your email or enroll you in marketing. For questions or deletion requests, email hello@seebeyond.ai. No files or sensitive business information are needed for this assessment.
START A CONVERSATION
Share the business task, your current stage, and the main concern: quality, cost, or implementation. No confidential documents are needed for the first conversation.
We’ll discuss fit, the inputs needed, and a useful scope. If there is a fit, the next step is a proposal setting out deliverables, fees, responsibilities, and acceptance criteria.
Not sure which offer fits? Start here and describe the problem in your own words.
AI evaluation, economics, consulting, training, and downloadable software.
Monday–Friday, 9 AM–5 PM Central
Closed Saturday and Sunday
