LLM development
NextPage builds LLM applications that connect models to real business context: retrieval systems, copilots, secure integrations, prompt workflows, evaluation, observability, and AI features inside existing software.
Built for
Founders, CTOs, product leaders, support leaders, and operations teams that need LLM systems connected to private knowledge, product workflows, APIs, permissions, and measurable outcomes.
An LLM roadmap tied to business workflow, data readiness, model choice, risk controls, and a first useful release.
RAG, copilot, agent, and LLM app features connected to documents, databases, APIs, product screens, and approval flows.
Production controls for answer quality, citations, cost, latency, permissions, monitoring, and continuous improvement.
Why this matters
The best outsourcing and software projects work because expectations, ownership, and delivery rituals are clear from the first week.
Teams have tried model demos, but the AI still cannot answer from current product data, policies, tickets, or internal documents.
Generic chatbots produce confident answers without citations, escalation paths, access controls, or evaluation coverage.
LLM costs and latency grow quickly when prompts, retrieval, caching, and model selection are not designed as product infrastructure.
Sensitive workflows need audit logs, role-based permissions, fallback behavior, and human review before AI can act.
Existing software needs AI features without a rewrite, including admin tools, CRMs, ERPs, support desks, and SaaS workflows.
Leaders need a clear build path that separates useful RAG and integration work from expensive foundation-model training.
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.
Plan the right LLM system before implementation starts, including use-case selection, model choice, data readiness, risk controls, and cost boundaries.
Build retrieval pipelines that let AI answer from your documents, policies, product data, support history, and operational knowledge.
Add LLM-powered experiences to SaaS products, internal tools, customer portals, dashboards, and mobile or web applications.
Create prompt systems and test sets that make model behavior easier to inspect, compare, and improve over time.
Use fine-tuning or smaller specialized models when retrieval and prompting are not enough for tone, classification, extraction, or domain behavior.
Connect LLM workflows to real systems with scoped permissions, logs, queues, review steps, monitoring, and clear fallback behavior.
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 map the buyer workflow, current systems, private data, user roles, risk level, integration points, and what the first LLM release must prove.
We build a narrow LLM or RAG slice with real sample data, evaluation questions, model comparisons, and a clear recommendation for production.
We connect the LLM workflow to product screens, APIs, databases, documents, queues, notifications, approvals, and analytics.
We monitor answer quality, sources, cost, latency, usage, edge cases, and feedback so the system improves after launch.
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 to choose the right LLM use case, understand data readiness, and avoid overbuilding the wrong model path.
Best when one high-value workflow needs a working proof with real documents, evaluations, and a path to product integration.
Best when LLM features are part of an ongoing product or operations roadmap and need engineering, QA, cloud, and support 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 LLM development company builds software that uses large language models inside real workflows. That can include RAG knowledge assistants, product copilots, customer support assistants, document automation, AI agents, prompt systems, model integrations, fine-tuning, evaluations, and monitoring.
Most businesses should start with model integration, retrieval-augmented generation, prompt design, and evaluation before training a custom model. RAG is usually better when answers must come from your documents, policies, product data, or support history. Fine-tuning is useful when the model needs specialized tone, classification, extraction, or domain behavior that prompting and retrieval cannot solve.
Yes. We can add LLM features to existing SaaS products, portals, admin panels, CRMs, ERPs, support workflows, mobile apps, and internal tools through APIs, retrieval layers, background jobs, and user-facing interfaces.
We reduce hallucinations with retrieval design, source-aware answers, prompt constraints, evaluation datasets, answer checks, fallback behavior, logging, human review for sensitive actions, and ongoing feedback loops.
Useful data can include product documentation, support tickets, policies, website content, CRM records, operational databases, PDFs, spreadsheets, call transcripts, or labeled examples. The first step is checking quality, permissions, freshness, and whether the data supports the target workflow.
Model choice depends on privacy, cost, latency, accuracy, multimodal needs, and deployment constraints. We can work with OpenAI APIs, Anthropic Claude, Google Gemini, open models, vector search, PostgreSQL, document pipelines, LangChain-style orchestration, and custom application infrastructure.
A focused discovery sprint or prototype can start small, then expand into production. Timeline depends on data access, integrations, UX complexity, evaluation needs, security controls, and the number of workflows the LLM system must support.
We define success with business and system metrics such as answer acceptance, deflection rate, task completion, processing time saved, source accuracy, escalation rate, latency, cost per workflow, and user feedback.
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.