AI agents and workflow automation
Agents that handle repeatable tasks with guardrails, logs, and clear handoff points.
- Task orchestration
- Tool/API integration
- Human-in-the-loop review
AI development services
AI development services for useful automation, agents, and LLM products
We help companies turn AI ideas into production workflows: AI agents, LLM apps, RAG systems, internal assistants, customer support automation, and AI features inside existing software.
Built for
Leaders who want practical AI outcomes, not a science project that never reaches production.
15+
years building software
15M+
users served across products
$50M+
value generated through platforms
India
engineering team with global delivery
AI workflows connected to real business processes.
LLM and agent systems designed with evaluation, fallback, and human review.
Production delivery from a team that also understands software engineering fundamentals.
Why this matters
Problems we remove before they become expensive
The best outsourcing and software projects work because expectations, ownership, and delivery rituals are clear from the first week.
AI experiments are scattered across tools and do not connect to real workflows.
Teams want automation but need governance, review, and measurable business value.
Your data is useful, but it is not organized for retrieval, reasoning, or decision support.
Generic chatbots are not enough for your domain or customer journey.
You need engineers who can connect AI to product, backend, security, and UX.
You want to move fast without putting sensitive data or business logic at risk.
What we build
A focused scope for this service
We shape the scope around the result you need, the systems you already have, and the first release that can create value.
LLM and RAG applications
Knowledge systems that answer using your documents, data, policies, and product context.
- Retrieval pipelines
- Prompt and response design
- Evaluation and accuracy checks
AI product features
Add summarization, recommendations, search, chat, classification, and copilots to existing products.
- Frontend UX for AI features
- Backend integration
- Monitoring and cost controls
AI search optimization
Improve how your brand and pages are represented in answer engines and AI-assisted search.
- GEO and AEO foundations
- Schema and content structure
- Entity and topical authority
Delivery model
How we turn the first call into a working system
We keep discovery practical, ship in visible increments, and make ownership clear so you can scale with confidence.
1
Discovery
We map the business goal, users, constraints, current stack, risks, and fastest useful first release.
2
Plan
You get a practical roadmap with scope, milestones, team shape, communication rhythm, and success metrics.
3
Build
We ship in visible increments with design, engineering, QA, demos, and code reviews built into the cadence.
4
Scale
We keep improving performance, reliability, features, and team capacity as the product starts moving.
Engagement options
Flexible enough for a project, stable enough for a long-term team
Choose the model that fits your current stage. We can start small, add specialists, or run a full product pod.
Scoped sprint
Best for discovery, MVP planning, prototypes, audits, or a tightly defined release.
- Fixed deliverables
- Weekly checkpoints
- Clear handoff
Dedicated pod
Best when you need consistent product velocity without hiring a full in-house team.
- Developers, QA, and PM support
- Sprint rituals
- Monthly capacity planning
Long-term partner
Best for companies that want a reliable India team for ongoing software and AI delivery.
- Roadmap ownership
- Maintenance and scaling
- Specialists added as needed
Proof
Product experience behind the services
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
Questions companies usually ask first
Clear answers help you understand how the engagement works before we get on a call.
What AI projects are a good fit?
Good fits include AI agents, customer support assistants, internal knowledge assistants, RAG systems, LLM-powered SaaS features, and workflow automation.
Do you only build with one AI model?
No. The model choice depends on cost, latency, privacy, accuracy, and tool ecosystem. We can work with OpenAI, Anthropic, Gemini, open models, and hybrid approaches.
How do you reduce AI risk?
We design guardrails, logging, fallback behavior, evaluation checks, scoped permissions, and human review for sensitive workflows.
Can AI be added to existing software?
Yes. Many useful AI projects start by adding focused capabilities to an existing product or workflow rather than rebuilding everything.
Next step
Tell us what you want to build. We will map the first practical plan.
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.