DEPLOYPROAI ENGINEERING · AI TRANSFORMATION · AI ECONOMICS AND AIOPS
Know what AI costsand what it returns.
Unit economics per call, per session and per workflow, then the operations layer that keeps agents observable, governed and within budget once they are live. Measured with DORA, SPACE and DevEx.
- Unit economics
- Token & inference cost
- Observability
- Evaluation in production
- Incident & escalation
- Model routing & fallbacks
The problem we solve
The inference bill arrives after the feature ships, not before.
- Only 37 percent of organisations report any positive EBIT contribution from AI.
- 95 percent of enterprise GenAI pilots showed no measurable profit impact, in a 2025 MIT Media Lab study of 150 leaders.
- An agent that fails silently costs more than one that never ran.
- Nobody owns the model bill, so nobody notices when it doubles.
Step 1
Baseline the cost
What the process costs today in people, time and tooling, on the metrics your business already tracks.
Step 2
Model the unit economics
Cost per call, per session and per workflow at your expected volume, against the value each one returns.
Step 3
Instrument production
Tracing, evaluation and alerting on every agent action, in the observability stack you already run.
Step 4
Operate and review
A monthly review of cost, quality and outcome against the baseline, with the decision to scale, continue or stop.
What we build
What we put in place.
Cost model per workflow
Token, inference, tooling and people cost per unit of work, before launch and tracked after.
Model routing and fallbacks
The cheapest model that meets the quality bar for each step, with a fallback when it does not.
Observability and tracing
Every agent action traced end to end, in your existing monitoring, so a failure is visible before a customer reports it.
Evaluation in production
The same evaluation set that approved the agent, run continuously on live traffic.
Incident and escalation
Alert thresholds, a human escalation path and runbooks for the failure modes the research phase found.
Monthly cost and outcome review
Spend, quality and the business metric side by side, with a written recommendation.
What you walk away with
Deliverables. Not promises.
A cost model per call, per session and per workflow
Dashboards and traces in your own observability stack
An alerting and escalation policy, with runbooks
An evaluation suite running on production traffic
A monthly cost and outcome review
A written budget guardrail per workflow
Production proof
Evidence before adjectives.
The result
50% to 80.1%
Call quality on one insurance client's line rose from 50% to 80.1% over three quarters, on an AI-native quality platform now in production.
In their words
While the world chases what AI can do, DeployProAI focuses on where it fails, cutting through the noise to deliver unbiased, ROI-driven solutions. Lean, AI-native systems that maximise impact with minimal spend.
FAQs
Frequently asked questions
DeployProAI’s pricing depends on the type of work:
- Engineering: priced per engagement, against the target we agree with you. Ongoing support is priced by the number of days per month.
- Upskilling for companies: a fixed fee per pilot, with package pricing for larger programmes.
- Programmes for universities: priced per group of participants.
- SupportSight and Rehearse: a one-time set-up fee, plus an annual licence based on the number of mailboxes (SupportSight) or sales representatives (Rehearse).
Every proposal states the scope and the price before any work begins. If you tell us about your process and your team, we will send you a price range within one working day.
For details, please see How we price.
DeployProAI measures your process before work begins and again at the end, using the measures your business already tracks.
- Software teams: the industry-standard DORA, SPACE and DevEx measures of delivery speed, quality and developer experience.
- Business teams: time taken, turnaround and quality.
The method and the raw figures are available under NDA.
For details, please see the G.U.I.D.E. method.
A person reviews its work. Every agent DeployProAI builds is tested before release, operates within defined limits and keeps a record of its actions. Any work it is uncertain about is passed to a person, together with its reasoning. No stage proceeds without a person’s approval.
Start with the outcome.
Tell us what the process costs you today. We will tell you what it would cost with an agent in it, and how we would keep it there.
- NDA-first
- Your codebase
- Measured with DORA and SPACE
- GDPR
- DPDP
- SOC 2 practices
- PCI-DSS
- IRDAI-aware
- EU AI Act