LLM applications and copilots
Build AI features inside the software your teams or customers already use.
- AI copilots for SaaS products
- Summarization and drafting workflows
- Model orchestration and API integration
Generative AI development
Generative AI development for production workflows, agents, and LLM products
NextPage builds generative AI applications that connect to real business work: LLM apps, AI agents, RAG systems, copilots, customer support assistants, content workflows, and automation with evaluation and human review built in.
Built for
Founders, CTOs, product leaders, and operations teams that want useful generative AI in production, not a disconnected prompt experiment.
20+
years building software
15M+
users served across products
$50M+
value generated through platforms
India
engineering team with global delivery
A generative AI roadmap tied to real workflows, data readiness, and measurable business value.
LLM apps, agents, copilots, and RAG systems designed with evaluation, guardrails, and fallback behavior.
A production engineering path that covers UX, backend integration, security, monitoring, and iteration after launch.
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 useful in demos but do not connect to customer, support, product, or operations workflows.
Teams have documents, tickets, policies, product data, and CRM context, but no reliable retrieval layer for AI answers.
Generic chatbots cannot handle your domain, permissions, handoffs, or escalation rules.
Leaders need model choice, cost controls, logging, fallback behavior, and human review before rollout.
Product teams want copilots and LLM features without slowing the core roadmap or risking user trust.
The business needs engineers who can connect prompts, models, data, UX, APIs, security, and production monitoring.
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.
RAG and knowledge assistants
Create systems that answer from your documents, product data, policies, tickets, and operational knowledge.
- Retrieval pipelines and embeddings
- Source-aware answers
- Permission-aware knowledge access
AI agents and workflow automation
Automate repeatable tasks with scoped tools, review steps, logs, and clear human handoff.
- Tool and API calling
- Task routing and approvals
- Human-in-the-loop review queues
AI chatbots and support assistants
Improve support, onboarding, and internal help workflows with assistants that know when to escalate.
- Customer support automation
- Internal helpdesk assistants
- Fallback and escalation design
Evaluation, guardrails, and rollout
Make generative AI safer to ship by testing answer quality, logging behavior, and monitoring cost and risk.
- Prompt and retrieval evaluation
- Scoped permissions and audit trails
- Cost, latency, and quality monitoring
Content and operations automation
Use generative AI for structured content, research, data entry, reporting, and repetitive operations.
- Content workflow automation
- Report and insight generation
- Back-office task acceleration
Technology stack
Technology stack we can shape around your product
The exact stack depends on the roadmap, but these are the common layers we plan across web, mobile, backend, cloud, data, QA, and AI-enabled workflows.
Frontend and mobile
Interfaces for customer-facing products, portals, dashboards, and mobile experiences.
NX
Next.js
SEO-ready web apps
RC
React
Reusable UI systems
TS
TypeScript
Safer product code
RN
React Native
Cross-platform apps
Backend and data
APIs, databases, jobs, integrations, and admin workflows behind the product.
Node.js
APIs and services
PY
Python
Automation and AI services
PostgreSQL
Product data
MySQL
Business data
Cloud, QA, and AI
Delivery systems that keep releases visible, tested, observable, and ready for AI features.
Docker
Portable services
GitHub Actions
Release workflows
Playwright
Browser testing
OpenAI APIs
AI product features
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
Discover
We identify the workflow, users, source data, risk level, and the first AI use case worth shipping.
2
Design
We map model choice, retrieval, prompts, UX, permissions, evaluations, cost controls, and human review.
3
Build
We implement the LLM app, agent, RAG pipeline, chatbot, or copilot with API and product integration.
4
Improve
We monitor usage, answer quality, edge cases, latency, and costs so the system gets better after launch.
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.
AI discovery sprint
Best when you need to choose the right generative AI use case before investing in a full build.
- Workflow and data audit
- Use-case ranking
- Build roadmap
Prototype to first release
Best when you have a clear AI idea and need a usable product slice with evaluation and integration.
- LLM/RAG prototype
- UX and backend integration
- Launch checklist
Dedicated AI product pod
Best when generative AI is becoming part of your product or operations roadmap.
- Engineers, QA, and PM support
- Iteration rhythm
- Monitoring and support
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 does generative AI development include?
Generative AI development can include LLM apps, AI agents, RAG systems, chatbots, copilots, workflow automation, prompt and retrieval design, model integration, evaluations, guardrails, deployment, and ongoing improvement.
How is generative AI development different from general AI development?
General AI development can include prediction, classification, analytics, and machine learning. Generative AI development focuses on systems that generate or transform text, images, content, decisions, summaries, and actions using LLMs and related models.
Can you build generative AI with our existing data?
Yes, if the data is accessible and useful for the workflow. We can connect documents, databases, tickets, product data, policies, APIs, and internal knowledge through retrieval, permissions, and integration layers.
Which AI models do you use?
Model choice depends on privacy, cost, latency, accuracy, hosting, and tool needs. We can work with OpenAI, Anthropic, Gemini, open models, and hybrid approaches where they fit the product requirements.
How do you reduce hallucinations and AI risk?
We reduce risk with retrieval design, evaluation checks, scoped permissions, logging, fallback behavior, source-aware answers, review queues, and human approval for sensitive workflows.
How long does a generative AI project take?
A discovery sprint or prototype can often be scoped first, then expanded into a production release. The timeline depends on data readiness, integrations, risk controls, UX complexity, and how many workflows the AI system must support.
Can generative AI be added to an existing SaaS product or internal tool?
Yes. Many strong projects start by adding copilots, summarization, support assistants, search, drafting, or automation to an existing product instead of building a separate AI app.
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.