15+
years building production software
Custom AI agents, cloud agents, and agentic AI workflow automation
Build custom AI agents that automate real business workflows
NextPage designs and builds cloud-ready AI agents that can use tools, connect to APIs, retrieve business knowledge, request approvals, and operate with the guardrails your team needs.
Custom AI agentsCloud agentsAgentic AI workflowsLLM appsRAG and knowledge agentsBusiness automationAPI integrationsHuman review
Cloud agent blueprint
Strategy ready01
Workflow
Define the task, user, handoff, and measurable result.
02
Tools
Connect approved APIs, systems, files, and business data.
03
Controls
Add permissions, logs, review gates, and fallback behavior.
04
Cloud
Deploy, monitor, evaluate, and improve the agent over time.
15M+
users served across NextPage platforms
APIs
custom integrations, data flows, and internal systems
Cloud
deployment planning for reliable agent workflows
Business outcomes
Agents become valuable when they fit the workflow
The first version should be narrow enough to trust and useful enough to change how work moves through the team.
Move repeatable work out of inboxes
Turn repetitive requests, updates, research, summaries, routing, and follow-ups into supervised workflows that your team can trust.
Connect AI to the systems people already use
Agents become useful when they can read context, call approved tools, update records, and hand work back to humans at the right moment.
Launch with controls, not loose demos
We plan permissions, logs, fallback paths, evaluation checks, and cloud deployment before an agent starts touching production workflows.
Use cases
AI agents for teams that need more than a chatbot
We focus on agents that can read context, prepare work, call approved tools, and route decisions with clear ownership.
Customer support agents
Triage tickets, summarize history, draft replies, pull policy answers, and escalate sensitive conversations.
Faster response prep with clearer handoffs.
Sales and CRM assistants
Research accounts, enrich leads, draft outreach, log activity, and keep pipeline notes current.
Less manual CRM work and better sales context.
HR and recruiting agents
Screen intake details, answer policy questions, schedule interviews, and prepare candidate briefs.
More consistent people operations workflows.
Finance operations agents
Extract invoice data, flag exceptions, prepare reconciliations, and route approvals for review.
Controlled automation for back-office tasks.
Document processing agents
Read contracts, forms, reports, and PDFs, then summarize, classify, compare, or extract structured fields.
Quicker document review without losing traceability.
Data analysis agents
Query approved datasets, explain trends, generate summaries, and prepare dashboards or decision briefs.
Operational answers without waiting on every report.
Monitoring agents
Watch queues, alerts, forms, or system events and recommend the next action when thresholds are crossed.
Earlier issue detection and clearer response paths.
Internal copilots
Give teams a secure assistant over company docs, product data, procedures, and customer context.
Better self-serve answers inside the company.
Cloud agent architecture
A production agent is a system, not a prompt
We design the model, tools, retrieval, permissions, evaluation, and cloud operations together so the agent can survive real use.
Models and prompts
Choose model providers and prompt patterns around latency, cost, privacy, context length, and task accuracy.
Tools and APIs
Expose only approved actions through typed tools, service APIs, webhooks, and workflow queues.
Knowledge and memory
Connect documents, databases, product records, policies, and retrieval pipelines with clear freshness rules.
Permissions and approvals
Scope what an agent can see or change, then add review steps for sensitive or irreversible actions.
Evaluation and logs
Capture traces, decisions, sources, failures, and review feedback so the system improves with evidence.
Cloud operations
Plan deployment, observability, scaling, secrets, fallback behavior, and monitoring for production use.
Integrations
Connect agents to the software your business runs on
We frame integrations as controlled capabilities: what the agent can read, what it can update, and what still needs human approval.
Customer and revenue systems
- CRM
- lead forms
- calendars
- sales pipelines
- payment systems
Team collaboration
- Slack or Teams
- Google Workspace
- Microsoft 365
- file storage
- project tools
- notifications
Operations and data
- databases
- ERPs
- analytics tools
- internal dashboards
- document stores
- custom APIs
Security and governance
Agents need boundaries before they need autonomy
When agents can touch business systems, the implementation has to make access, review, and recovery explicit.
Scoped permissions for every tool and data source
Human-in-the-loop approval for high-risk actions
Audit logs for prompts, tool calls, outputs, and failures
PII-aware workflow design and safer data handling paths
Secrets management for API credentials and environment access
Fallback behavior when confidence, data quality, or system availability is not good enough
Delivery process
From workflow idea to monitored cloud agent
We keep the first release grounded in real inputs, accessible systems, success metrics, and a review path your team accepts.
1
Discover
Map workflows, users, systems, sensitive actions, current bottlenecks, and the first useful agent outcome.
2
Architect
Design the model, tools, knowledge sources, permissions, cloud deployment, and success metrics.
3
Prototype
Build a narrow proof of concept with real inputs, review checkpoints, and measurable task quality.
4
Integrate
Connect approved APIs, databases, communication tools, documents, and business systems.
5
Evaluate
Test outputs, edge cases, fallback paths, latency, costs, security boundaries, and human review flows.
6
Launch and improve
Deploy safely, monitor usage, review traces, tune workflows, and add capabilities as confidence grows.
Where it fits
Practical agent ideas across product, service, and operations teams
The strongest use cases usually start with one workflow, one owner, and enough examples to evaluate quality before launch.
SaaS teams automating onboarding, support, product analytics, and customer-success tasks.
Service businesses routing leads, drafting proposals, summarizing calls, and preparing follow-ups.
Operations teams processing documents, approvals, inventory updates, and exception handling.
Marketplaces and content platforms improving moderation, research, categorization, and internal tooling.
Why NextPage
Software engineering first, AI where it creates leverage
Agent projects need product judgment, backend engineering, cloud operations, and integration discipline as much as model selection.
Agent scope tied to a measurable workflow, not a vague AI experiment.
Cloud-ready implementation that can live inside your existing product or operations stack.
Integration-first engineering for APIs, databases, documents, and the tools your team already uses.
Governance patterns that keep humans in control where risk, cost, or customer trust matters.
FAQ
Questions teams ask before building AI agents
What is a custom AI agent?
A custom AI agent is software that uses an AI model plus approved tools, data sources, instructions, and safeguards to complete a business workflow. It can read context, reason through the next step, call APIs, draft outputs, and ask for human review when needed.
How is an AI agent different from a chatbot?
A chatbot mostly answers questions. An AI agent can be designed to take controlled actions, use tools, update systems, retrieve business context, follow workflow rules, and hand off decisions to people when the action is sensitive.
Can an agent connect with our CRM, email, documents, or internal software?
Yes. Most useful agents depend on integration work. We can connect agents with CRMs, email, calendars, collaboration tools, office suites, databases, ERPs, file storage, analytics systems, and custom APIs when access and permissions are available.
How do you handle security and data privacy?
We design scoped permissions, credential handling, audit logs, human approvals, fallback paths, and PII-aware workflows. Sensitive actions should be reviewed or tightly controlled instead of giving an agent broad access by default.
How long does an AI agent project take?
A focused prototype can often be planned and built in a short sprint once the workflow, data sources, and tool access are clear. Production timelines depend on integrations, review requirements, security constraints, and how many workflows the agent needs to handle.
What do we need before starting?
The best starting point is one valuable workflow, sample inputs, the systems involved, success criteria, and a clear view of which actions need human approval. From there we can define the first prototype and deployment path.
Next step
Bring one workflow. We will map the first agent your team can trust.
Share the business task, the tools involved, the risk level, and what success should look like. We will help you shape a practical prototype plan.