AI Agent Pilot Implementation
From workshop to a working agent — one fixed-scope pilot, delivered in four to six weeks.
The AI Agent Pilot Implementation converts a validated use case into a production-grade AI agent operating on your actual business workflow. Each pilot is scoped to a single, clearly defined process — for example, automated lead qualification from inbound channels, document classification for financial operations, or a knowledge-base assistant for internal teams. This deliberately narrow scope ensures we deploy working software within six weeks, not an ever-expanding roadmap.
Our delivery methodology integrates four disciplines: AI engineering (model selection, prompt design, evaluation harness), data engineering (pipelines, embedding, retrieval), backend integration (API contracts with existing systems), and quality assurance (automated test suites, human-in-the-loop validation). Every pilot includes a monitoring dashboard so your team can observe agent decisions, intervention rates, and accuracy metrics from day one of production.
The pilot includes full documentation, a technical handover, and a 30-day post-deployment hyper-care period. After that period, clients typically transition to the Managed Service tier for continuous optimization, or operate independently using the runbooks and architecture diagrams we provide. We design every pilot for transferability — your team is never locked in technologically.
This engagement is right when…
Organisational signals that indicate this tier is the relevant entry point.
— One AI agent use case has been validated through internal or external diagnostics — ready to move from concept to production.
— The target workflow is clearly defined: input/output boundaries are known, success criteria are measurable, and data is available.
— The organisation is ready to host the agent on internal infrastructure or accept a managed deployment with an SLA.
— Delivery with a fixed scope commitment and hard deadline is required, not an open-ended engagement.
What you receive.
Concrete outputs from this engagement — fully documented, independently usable.
- 01 One AI agent deployed to production for the scoped workflow
- 02 Integration with up to three existing systems (CRM, ERP, messaging, etc.)
- 03 Evaluation harness with a regression test suite
- 04 Operations dashboard with accuracy and intervention metrics
- 05 Technical documentation, runbooks, and architecture diagrams
- 06 30-day post-deployment hyper-care with refinements
How the engagement works.
A structured engagement flow — each stage has a verifiable output.
- 01 Scope lock: finalization of agent boundaries, success metrics, and acceptance criteria
- 02 Data and architecture design: pipelines, retrieval strategy, integration points
- 03 Build sprint one: core agent logic, prompt engineering, evaluation framework
- 04 Build sprint two: integrations, monitoring dashboard, edge-case handling
- 05 User acceptance testing: structured validation with your operational team
- 06 Production deployment and 30-day hyper-care with weekly review cadence
Questions that frequently arise.
Clarifications on questions typically raised in initial discussions.
What if the diagnostic workshop was not conducted with PT Widigital Tri Buana? [ + ]
A pilot engagement can begin without a prior workshop with our team, but it requires a structured one-week scoping phase to validate the use case, data readiness, and integration feasibility. This phase is included in the engagement fee for clients bringing independently produced diagnostic outputs.
What if requirements change during the six weeks? [ + ]
Minor scope refinements are expected and accommodated within the engagement. Substantive scope changes — new workflows, new integrations, or new user personas — are handled through a change request process with transparent re-scoping and timeline adjustment. The fixed-scope model is designed to protect both parties from open-ended delivery.
Where is the agent deployed — on our infrastructure or yours? [ + ]
Both options are supported. The pilot can be deployed to your existing cloud account (AWS, Google Cloud, Azure), to your on-premise environment if it meets the technical prerequisites, or to PT Widigital Tri Buana's managed cloud infrastructure on a scalable edge computing platform. The hosting decision is made during the scope lock phase based on data sovereignty, security, and operational preference considerations.
Which AI models and platforms do you work with? [ + ]
Model selection is driven by use-case requirements. We work with frontier models (Claude, GPT-4 class, Gemini), open-source models (Llama, Qwen) for cost-sensitive or sovereignty-oriented deployments, and Indonesia-context-tuned models where available. The evaluation harness enables model swaps without rewriting the agent — protecting clients from vendor lock-in.
What level of accuracy can we expect from the agent? [ + ]
Acceptance criteria are established during the scope lock phase and typically target 85% to 97% accuracy depending on workflow complexity and risk tolerance. Every pilot includes a human-in-the-loop fallback path for low-confidence decisions, ensuring the agent does not produce silent failures in production.
What happens after the 30-day hyper-care period ends? [ + ]
Clients typically choose one of three paths: (1) transition to the Managed Service tier for ongoing monitoring and optimization, (2) operate the agent internally using the runbooks we hand over, or (3) commission an expansion engagement to widen the agent's scope. All three paths are supported without lock-in.
Once the pilot is live, the Managed Retainer maintains accuracy and expands capabilities monthly.
Proceed to Managed Retainer — Agentic AI Managed ServiceReady to begin pilot?
Discuss your organisation's AI agent requirements with the PT Widigital Tri Buana team. Response within one business day.