Attributable cost
Model and infrastructure spend, retries, agent loops, excessive context, failed work, and relevant human review or support.
AI cost to outcome diligence
Know what the workflow costs. Test what can change.
Our AI cost to outcome diligence helps consultancies understand the full cost of an operating AI workflow, identify avoidable spend, and test scoped changes against the quality and response time the client needs. You keep the client relationship and implementation.
Case studies
3 projects. ₹2,40,720 in projected annual savings.
What three projects could save over a full year, based on their 30-day results. Lower running costs for customer support, purchase orders, and company research. Company names are kept private.
Customer support
₹67,200
Projected annual savings
Lower costs for preparing support replies, with every reply still approved by a person.
Read case study Purchase orders₹41,040
Projected annual savings
Less spent processing orders, with more drafts ready for approval.
Read case study Company research₹1,32,480
Projected annual savings
Lower research costs and faster briefs, with source checks and human review retained.
Read case studyAnnual projections multiply each 30-day saving by 12, assuming the same workload and costs throughout the year. These are savings in running costs before review fees and implementation costs; staff time is excluded.
Who it is for
For consultancies carrying the operating burden.
The review fits a live workflow with enough spend, support effort, or client exposure to justify investigation. The consultancy needs evidence access, implementation authority, and a clear view of who benefits from any reduction.
Bounded initial review
One operating workflow, with scope and timing agreed before work begins.
Activation requires an agreed scope, payment condition, named owners, and the minimum evidence pack. Low spend or weak evidence may mean a paid review is not worthwhile. The fixed scope and fee are confirmed after qualification.
What comes back
- Baseline workbook with measured, client-supplied, and assumed inputs separated
- Cost per successful task for a representative period
- Prioritised, editable recommendations with expected mechanism and trade-offs
- Test record against agreed quality and response-time gates when testing is in scope
- Implementation handoff showing what the consultancy owns
- Verification plan with a separately agreed post-deployment window
How it works
Baseline, review, recommendations, verification.
We agree the review timeline with your team before starting. After your team makes the changes, results can be checked over a separately agreed period.
01
Baseline
Define the successful task and representative period. Separate measured facts, client-supplied inputs, and assumptions, then normalise for volume and task mix.
02
Review and test
Trace spend through calls, context, retries, failures, infrastructure, and support. Run limited approved tests only when the scope and access permit them.
03
Recommend and verify
Receive editable recommendations and a verification plan. Your team implements approved changes; NASC can verify results later under a separate scope.
What is reviewed
Dashboards show spend. The workflow explains it.
Provider dashboards, routers, and caches are useful inputs. The review adds workflow-specific judgement about successful work, avoidable calls, failure handling, human effort, and client requirements.
Successful task
The agreed unit of useful output, its quality threshold, response-time requirement, and the evidence needed to count it.
Workflow control
Routing, caching, batching, fallbacks, call limits, permissions, and operational ownership. Existing tools are inputs, not a substitute for workflow judgement.
Safe testing
Limited tests use approved data and non-production access only when explicitly scoped. Otherwise recommendations are marked unvalidated.
AI design and development
Turn a business problem into working software.
Have a business problem that needs an AI solution? NASC can design and develop it, from architecture and cost planning through implementation, testing, and deployment.
Separate engagement
AI Design & Development
We turn an agreed business problem into a delivery plan, then build and integrate the solution. We agree scope, milestones, costs, and acceptance criteria before development begins, and deliver tested software with deployment, documentation, and handover.
What comes back
- Reference architecture and system boundaries
- Data, integration, and permission flows
- Delivery plan with build costs and twelve-month running-cost estimates
- Working AI solution with agreed integrations
- Testing against agreed quality and acceptance criteria
- Deployment, source code, documentation, and handover
Design and development is scoped separately from AI cost to outcome diligence. When NASC designs or builds a solution, our assessment of that work is delivery assurance. Independent diligence requires a separate reviewer.
Illustrative founder-owned review
Tweet Helper.
A transparent planning example for an NASC product. It is not an independent paid-client case, and the figures are assumptions rather than achieved savings.
Decision
Make every AI pass earn its place
The example tests whether routine automation can stay deterministic, feed scoring can be batched, and optional model passes earn their cost. Actual results require production evidence after implementation.
- Feed posts per AI batch
- Up to 24
- Repair attempts
- Maximum 1
- Expected acceptance
- 65% planning case
- Cost basis
- Accepted result