AI SPEND OPTIMIZATION / WORKFLOW CONSULTING
Your AI bill is growing faster than your output.
We help mid-market teams reduce token waste and build workflows that hold up in production. Start with your AI spend. Fix the system behind it.
ENGINEER-LED
INDUSTRY EXPERIENCED
NORTH AMERICA
01 / SOUND FAMILIAR?
Different seats. The same friction.
AI adoption moves fast. The operating systems around it don’t always keep up.
VP Sales
More tools. Less visibility.
Your team is paying for AI, but you can’t connect the spend to the sales workflow.
Marketing Director
The CRM needs a cleanup.
HubSpot automations multiply. Data quality slips. Nobody trusts the handoff.
COO
The workflow won’t scale.
What worked for one person now depends on manual fixes across three teams.
CTO
The stack has no clear owner.
Model calls, integrations, and costs are spread across systems nobody sees end to end.
02 / OUR PRACTICES
Start with the bill. Improve the whole workflow.
Concrete changes to token usage, operations, and the tools your team relies on.
01 / AI Spend Audit
Find out where your tokens go.
A usage baseline by workflow, model, and team. Identify repeated context, unnecessary calls, and spend without a clear owner.
02 / Workflow Redesign & Model Routing
Use the right model for the job.
Route routine tasks to smaller models, reserve larger models for the work that needs them, and test the difference.
03 / Caching, Evals & Guardrails
Reduce repeat work. Protect output quality.
Cache stable inputs, build representative evals, and set limits on retries, context, and tool calls.
04 / Ongoing Cost Monitoring
Catch cost drift before the next invoice.
Track usage and cost per successful task, with thresholds and ownership your team can maintain.
05 / HubSpot Implementation
Make your CRM match how your team works.
Clean up lifecycle stages, routing, data quality, and automation so sales and marketing work from the same system.
Connect the tools. Remove the workarounds.
Map data flows, repair handoffs, and evaluate technology purchases against actual operating requirements.
03 / HOW WE WORK
A clear scope. An accountable build.
One-off projects to solve a defined problem. Ongoing support only if you need it.
01
Audit
Map usage, costs, data flows, and the work that matters.
02
Plan
Prioritize changes by impact, effort, and risk. Agree on scope.
03
Implement
Build, test, and document the changes inside your stack.
04
Monitor
Measure the result and hand over clear ownership.
04 / THE PEOPLE DOING THE WORK
Operators, not spectators.
Approach Works is led by a solutions engineer and a forward-deployed engineer. Our day-to-day work takes us inside companies’ AI systems and technology stacks. We bring that hands-on perspective to your workflows, from the first audit through implementation.
Two founders. Direct involvement. No handoff to a separate delivery team.
05 / BEFORE WE TALK
Good questions. Straight answers.
How long does an AI spend audit take?
Timing depends on the number of workflows, providers, and the availability of usage data. We agree on a delivery schedule after reviewing the scope, before work starts.
What do you need from us?
A workflow owner, an overview of your stack, and relevant usage or billing exports. We agree on access and data-handling boundaries first; sensitive data should be redacted where possible.
What does it cost?
Projects are scoped and priced after discovery. You receive defined deliverables and a proposal before committing. A retainer is optional, not a condition of getting started.
Do you work with our existing stack?
We start with what you already use. We review model providers, orchestration, HubSpot, and integration constraints before recommending changes or new tools.
Who owns the work afterward?
You own the project deliverables. We document the configuration, implementation, and operating decisions so your team can run the system without depending on us.
FIELD NOTES
Useful before you hire anyone.
Practical answers to specific operating questions.
Token cost
How do you reduce AI token costs without lowering quality?
Start by measuring cost per successful task, then reduce repeated context, unnecessary output, and oversized model calls. Validate every change against representative evals.
AI workflows
When should you add model routing to an AI workflow?
Add model routing when your workflow contains tasks with different complexity or quality requirements. First establish an eval set so the routing decision can be tested.
HubSpot
What should you fix in HubSpot before adding AI?
Fix lifecycle definitions, required fields, record ownership, and duplicate handling before adding AI to HubSpot. Automation built on inconsistent records spreads the inconsistency faster.
LET’S FIND THE FRICTION
Less waste. Better systems.
Tell us what you’re running, what’s getting expensive, and where work gets stuck.