AI Agent PoC Sprint
NextPage runs a focused AI agent PoC sprint that ranks use cases, checks data and integration readiness, defines guardrails, scopes a prototype, and turns agentic AI interest into an evidence-based build plan.
Built for
Teams that know AI agents could help but still need a narrow first use case, data-readiness score, integration map, guardrails, prototype scope, and ROI assumptions before a larger build.
A ranked shortlist of 3-5 AI agent use cases with readiness, risk, implementation complexity, and expected business value clearly compared.
A practical prototype plan covering workflow steps, source data, system integrations, evaluation examples, guardrails, and success metrics.
A decision-ready handoff for whether to build a controlled prototype, prepare data and APIs first, or defer ideas that are not ready.
Why this matters
The best outsourcing and software projects work because expectations, ownership, and delivery rituals are clear from the first week.
The team has several AI agent ideas but no shared way to compare value, complexity, risk, and readiness.
Workflow data, documents, permissions, APIs, and owners are spread across tools, making demos look easier than production.
Leaders want ROI evidence before funding an agent build, but the baseline time, error, volume, and escalation data is not yet organized.
Generic agentic AI pitches skip human review, audit logs, fallback behavior, and the actions an agent must never take alone.
A full build feels risky because scope, data quality, integrations, model choice, and security boundaries have not been tested with real examples.
The first AI agent must be useful enough to prove value but narrow enough to ship safely and learn quickly.
What we build
We shape the scope around the result you need, the systems you already have, and the first release that can create value.
Compare candidate workflows by business value, frequency, exception handling, data availability, integration effort, and risk so the first sprint targets the right problem.
Map the documents, databases, APIs, tools, permissions, owners, and edge cases the agent would need before any prototype is treated as production-ready.
Define what the agent can read, draft, recommend, update, or escalate, including approval points and fallback behavior for low-confidence or high-risk cases.
Turn the chosen workflow into a controlled PoC plan with user journeys, prompt and retrieval approach, tool calls, test examples, acceptance criteria, and handoff notes.
Estimate whether the workflow is worth building by comparing current effort, volume, error cost, delay cost, adoption constraints, and realistic automation potential.
Leave the sprint with enough product and technical detail for a controlled agent pilot, a data-readiness backlog, or a larger production roadmap.
Technology stack
We choose AI tools around the workflow, data sensitivity, latency, model quality, integration depth, and operating cost. The result is an AI system your team can evaluate, monitor, and improve.
Model choices for copilots, agents, retrieval workflows, classification, and content automation.
OpenAI APIs
LLM products and assistants
Anthropic Claude
Reasoning-heavy workflows
Google Gemini
Multimodal AI features
Open models
Private and specialized use cases
Retrieval layers that let AI answer from your policies, product data, documents, and support history.
Vector search
Semantic retrieval
PostgreSQL
Structured business data
Document pipelines
Ingestion and chunking
Evaluation sets
Answer quality checks
Controlled automation that connects AI decisions to tools, APIs, approvals, and operational workflows.
LangChain
Agent and chain patterns
Tool calling
System actions and APIs
Workflow queues
Reliable task execution
Human review
Sensitive workflow control
The application layer that makes AI useful inside software people already use.
Next.js
AI-enabled web apps
Node.js
APIs and integrations
Python
AI services and data work
Docker
Portable deployments
Controls for cost, quality, permissions, auditability, and safe fallback behavior.
Prompt logging
Debugging and audit trails
Cost controls
Token and usage visibility
Guardrails
Policy and output checks
Playwright
User-flow regression tests
Additional capability for prediction, scoring, recommendations, analytics, and model-backed decisions.
Machine learning
Prediction and scoring
Analytics
Adoption and outcome tracking
Data pipelines
Reliable inputs
Model APIs
Reusable AI services
Delivery model
We keep discovery practical, ship in visible increments, and make ownership clear so you can scale with confidence.
We collect agent ideas, current process notes, known systems, decision owners, constraints, and the outcomes leadership needs to validate.
We compare each use case across value, data quality, integration access, workflow clarity, governance needs, and prototype feasibility.
We define the selected workflow, sample inputs, retrieval needs, tool calls, human review points, evaluation set, and architecture sketch.
We package the sprint into a practical recommendation: build the controlled prototype, prepare missing data and APIs, or defer low-readiness ideas.
Engagement options
Choose the model that fits your current stage. We can start small, add specialists, or run a full product pod.
Best when you need a focused, decision-ready plan before funding a larger agentic AI implementation.
Best when one workflow is ready for hands-on validation with real examples, approved data, tool boundaries, and human review.
Best when the PoC proves value and you need AI engineering, integrations, QA, cloud, monitoring, and product iteration together.
Proof
NextPage is not starting from theory. The team has built and operated products, platforms, and internal systems with real users.
Maxabout: automotive platform with large-scale search traffic
NextBite: ordering workflows for food entrepreneurs
ChatRoll and OutRoll: communication and outreach products
FAQ
Clear answers help you understand how the engagement works before we get on a call.
An AI Agent PoC Sprint is a short discovery and prototype-planning engagement that helps a team choose the right first AI agent workflow, validate readiness, define guardrails, estimate ROI, and scope a controlled prototype before committing to a full build.
You get a use-case shortlist, readiness score, data and integration map, guardrail plan, prototype scope, evaluation examples, architecture sketch, ROI assumptions, and a recommendation for the next build step.
The sprint is designed to choose and scope the right prototype first. When the workflow is already clear and data access is available, the next step can be a controlled prototype build with a tight backlog and test set.
Good candidates are repeated workflows with measurable volume, available data or documents, clear owners, API or export access, a human review path, and enough business impact to justify a prototype.
We score real workflows, inspect data and integration constraints, define what the agent can and cannot do, create evaluation examples, and tie recommendations to ROI assumptions rather than generic AI demos.
Yes. The sprint is meant to de-risk full development by clarifying scope, architecture, guardrails, metrics, and team shape before moving into a controlled prototype or production AI agent build.
Next step
Share your goal, current stack, deadline, and team gaps. We typically respond within 24 hours.
Use the project form first
The form captures your goal, budget, timeline, and service context so we can route the lead, prepare properly, and keep follow-up inside the pipeline.