Use-case definition
Success metrics, data sources, human handoffs, and edge cases locked before build accelerates.
ASC Digital
AI implementations
From approved use case to production — models, workflows, integrations, and adoption in one delivery track.
Book a callAI & Automation
Ship AI that teams trust in production
ASC Digital implements AI solutions with the same discipline as CRM programs: clear requirements, secure architecture, staged go-lives, and instrumentation so you can prove value.
Delivery path
Six phases from idea to scale
Every implementation follows the same disciplined path. Scroll to walk through each phase and what it leaves behind.
01 Discover
Frame the use case
Agree the problem, the users, the success metric and what “good” looks like — including the cases the AI should not handle.
What you get
- Use-case brief
- Baseline metrics
- Risk notes
02 Design
Design the solution
Choose the platform, data sources and models, and decide where humans review. Security and access are designed now, not patched later.
What you get
- Solution architecture
- Data access plan
- Human-in-the-loop map
03 Build
Build in small slices
Short iterations with real users and real data. Evaluation sets are built alongside the feature, so quality is measured rather than guessed.
What you get
- Working increments
- Evaluation set
- CRM and tool integrations
04 Pilot
Pilot with a real team
A contained group uses it for real work. We track the agreed metric, collect feedback every day and fix what breaks.
What you get
- Pilot results
- Feedback log
- Go / no-go recommendation
05 Harden
Harden for production
Monitoring, fallbacks, versioning and runbooks turn the pilot into something your team can own — not a demo held together by a vendor.
What you get
- Monitoring and alerts
- Runbook and owners
- Rollback path
06 Scale
Scale what worked
Roll out to more teams, reuse components for the next use case, and feed the results back into your AI roadmap.
What you get
- Rollout plan
- Reusable components
- Next-wave backlog
Production readiness
Our go-live checklist
Nothing ships until every item below is true. Scroll down and watch the list close out.
Success metric instrumented
The KPI agreed in discovery is measured automatically, against a baseline.
Security and access reviewed
Permissions, data exposure and vendor terms checked and signed off by the right owner.
Evaluation set passing
The solution meets the quality bar on a fixed set of real examples, edge cases included.
Fallback path in place
If the AI is unavailable or unsure, work continues through a defined manual route.
Monitoring and alerts live
Errors, quality drift and usage are tracked, with alerts going to a named owner.
Prompts and models versioned
Every change is traceable and reversible, so improvements never become mysteries.
Users trained
The people using it know what it does, what it doesn't, and how to report problems.
Hypercare scheduled
A defined support window after launch, with daily check-ins in the first weeks.
How we implement
Delivery that survives go-live
Architecture & security
Identity, permissions, logging, and vendor choices aligned to your risk profile — SMB lean or enterprise-grade.
Integrate where work happens
CRM, collaboration tools, and back-office systems so AI sits in the flow of work — not a separate portal nobody opens.
Measure and harden
Pilot KPIs, feedback loops, and hypercare so the first win becomes a repeatable pattern.
Who we serve
Implementation shaped to your scale
For SMBs
One primary use case, a thin but solid foundation, and a go-live your team can operate without a standing AI office.
Typical: discovery → build → pilot → harden in one contiguous engagement.
For enterprises
Program delivery across business units, with architecture review boards, environment strategy, and integration into existing platforms such as Salesforce.
Typical: workstreams for data, experience, and change; phased production releases.
FAQ
Common questions
How long does a typical AI implementation take?
Focused SMB pilots often land in 4–12 weeks. Enterprise programs are phased — first production value in a similar window, then expansion across teams.
Do you build custom models or use platforms?
We prefer proven platforms and APIs when they meet the use case, and custom components only where differentiation or control requires it.
What do you need from us to start?
A clear problem statement, access to subject-matter experts, and visibility into the systems and data that will feed the solution.
Ready to move a use case into production?
Share the outcome you need. We outline scope, risks, and a delivery plan you can approve.