Enterprise AI consulting and implementation

Turn AI spend into productive growth.

Start with one revenue-critical workflow. Waddle Works helps your team increase throughput, shorten the path from opportunity to action, and measure whether the workflow can support growth before you scale it.

Growth through stronger teams, not fewer people.

Productive growth More revenue capacity without cost and complexity rising at the same rate

Productive growth, defined

Saved time is not the outcome. What the team does with it is.

A faster task matters when it helps the business respond to more qualified opportunities, improve a revenue decision, serve a customer sooner, or avoid spending that would otherwise be necessary.

More Revenue capacity
Better Unit economics
Productive Growth
01

Review more qualified opportunities

Increase the amount of useful demand your team can assess without lowering the standard for a good opportunity.

02

Shorten the path to a revenue decision

Reduce the time between a signal, a useful recommendation, and the person who owns the decision.

03

Respond to customers sooner

Remove avoidable waiting from quotes, proposals, support, and delivery workflows where speed affects the customer.

04

Grow capacity with the team you have

Give experienced people more room for judgment, relationships, and work that directly supports revenue.

DIY workflow scorecard

Is this workflow worth proving?

You do not need a model comparison to answer the first question. Use this checklist to decide whether the workflow deserves a pilot at all.

Good candidate

A repeated, revenue-relevant workflow with enough volume to measure and owners who can act on the result.

  1. Close to revenue

    The workflow affects demand, conversion, retention, customer delivery, or a decision about where to invest effort.

  2. Enough volume to matter

    It happens often enough that a better process can create capacity the business can use.

  3. A baseline you can compare

    Cycle time, throughput, quality, conversion, or cost can be measured before and after the pilot.

  4. Usable data and clear boundaries

    The team knows what information is available, what is sensitive, and where human approval must remain.

  5. Business and technology owners

    One person owns the growth result. Another owns integration, security, and the technical path forward.

The growth workflow pilot

One bounded pilot. Five decision gates.

Each gate produces evidence the business and technology owners can inspect. The goal is a sound production decision, not a demo that has to be justified later.

  1. 01

    Define

    Choose the growth outcome

    Record the baseline, target measure, owners, constraints, and the evidence that would stop the work.

  2. 02

    Map

    Understand the real workflow

    Document the steps, decisions, data, integrations, risks, and places where people must remain responsible.

  3. 03

    Build

    Create the smallest useful pilot

    Use the simplest suitable models and components to test the workflow under realistic conditions.

  4. 04

    Evaluate

    Measure the result and the failure cases

    Compare throughput, cycle time, quality, cost, and required human review with the original baseline.

  5. 05

    Decide

    Scale, revise, or stop

    Leave with a production recommendation, implementation plan, handoff package, or evidence that further investment is not justified.

Growth economics

Growth that costs more than it creates is not growth.

The pilot measures both sides of the case. Revenue capacity matters. So do model spend, review effort, integration work, and the cost of mistakes.

01

Match the model to the work

Reserve expensive models for tasks that need them. Route simpler work to smaller options that meet the quality bar.

02

Automate inside clear boundaries

Automate repeatable steps. Keep people responsible for consequential decisions, exceptions, and relationships.

03

Translate time into business value

Count saved time only when it increases throughput, improves a customer outcome, avoids spending, or supports revenue.

What you receive

Evidence your team can use. Not a strategy deck that expires.

01

Business case

Outcome brief

Baseline, target measure, workflow owners, constraints, risks, and agreed stop conditions.

02

Working evidence

Bounded pilot

A functional workflow built against realistic inputs, clear human approvals, and the agreed technical boundaries.

03

Measurement

Evaluation record

Quality, throughput, cycle time, cost, failure cases, and review requirements compared with the baseline.

04

Next decision

Production recommendation

A direct recommendation to scale, revise, or stop, with architecture notes and a handoff path your team can own.

Working principles

  • Agree on data boundaries before access
  • Keep human approval where consequences matter
  • Track quality and cost together
  • Document decisions and failure cases
  • Transfer knowledge to the internal team
askaGOAT goat mark
askaGOAT

Working product evidence

A revenue decision workflow, built and running.

askaGOAT reviews dense government solicitations and produces a structured go or no-go brief in about a minute.

It is designed to help teams screen more opportunities and focus proposal effort on better-fit bids. It surfaces mandatory requirements, workload, and bid risks so the person responsible for the opportunity can make a faster, informed decision.

askaGOAT is product evidence, not a claim of customer revenue or conversion results.

Explore askaGOAT
Input Solicitation documents
Analysis Requirements, risks, workload
Decision Structured go or no-go brief

Start with one workflow

Bring the bottleneck. We will help you test the case.

A useful first conversation does not need a finished AI strategy. Bring the workflow, what slows it down, and the result the business needs.

Evaluate a growth workflow

Bring these six inputs

  1. 01The revenue-critical workflow
  2. 02The current bottleneck
  3. 03The business owner
  4. 04The technology owner
  5. 05A baseline or useful measure
  6. 06Known data constraints