ChatGPT-based software development services
ChatGPT app development
We build custom ChatGPT-based applications for internal teams, customer portals, SaaS products, and enterprise workflows.
Our team designs the application logic, user roles, data access rules, model routing, API integrations, and deployment setup, resulting in an LLM product that integrates seamlessly with your existing software environment.
RAG & vector database engineering
We do not rely only on what the model already knows. We build retrieval-augmented generation systems that connect the LLM to your company's knowledge.
Our engineers design ETL pipelines that extract, clean, chunk, embed, and index data from sources such as SQL databases, PDFs, SharePoint, Google Drive, Confluence, and internal documentation. The LLM retrieves relevant context before generating an answer, which makes the system more useful for company-specific tasks.
RAG development
ChatGPT integration
We integrate ChatGPT and other LLMs into existing web platforms, mobile apps, ERPs, CRMs, support systems, and analytics tools.
The work can include API design, authentication, logging, permission checks, admin panels, prompt management, monitoring, and fallback logic. We also connect the LLM to business systems so it can assist with tasks rather than only answer questions.
AI integration services
LLM-agnostic abstraction layers
We build a routing layer that can switch between OpenAI, Azure OpenAI, Anthropic Claude, self-hosted Llama-family models, and other LLM endpoints based on cost, latency, availability, and compliance needs. This reduces vendor lock-in and gives your team more control over operating costs.
LLM development
AI agent development
We build AI agents that can plan tasks, call tools, retrieve company knowledge, and interact with enterprise systems in accordance with defined rules.
These agents can support workflows such as quote generation, vendor comparison, document review, order processing, internal support, and report drafting. For sensitive actions, we add human approval steps before the agent writes data back to a system.
AI agent development
Security guardrails and prompt injection defense
We design middleware that checks user input, retrieved context, model output, and tool calls before they affect your application.
This can include prompt injection detection, PII masking, output validation, access checks, audit logs, rate limits, and blocked-action policies. The goal is to keep the LLM useful without giving it uncontrolled access to data or business operations.
| Wrapper approach | Dual-Engine LLM architecture |
|---|---|
| Static prompts with limited company context | Dynamic semantic retrieval from approved company sources |
| One model provider hardcoded into the app | Routing layer for OpenAI, Claude, Azure-hosted models, and self-hosted LLMs |
| Broad access to copied documents | Permission-aware retrieval with user-level access checks |
| Little visibility into hallucinations | Evaluation pipelines that score answer quality against the retrieved context |
| Prompt injection handled only through instructions | Input checks, output validation, tool permissions, and audit logs |
| Token costs grow with every repeated query | Token monitoring, caching, batching, and fallback rules |
| Hard to scale beyond a demo | Service architecture, CI/CD, observability, and support workflows |
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GenAI technology stack
Vector databases
- Pinecone
- Weaviate
- pgvector
- Elasticsearch vector search
Orchestration and agent frameworks
- LangChain
- LlamaIndex
- CrewAI
- Semantic Kernel
LLMOps and evaluation
- LangSmith
- TruLens
- RAGAS
- custom evaluation pipelines
Inference and model routing
- LiteLLM
- vLLM
- OpenAI
- self-hosted open-source models
Business benefits of custom ChatGPT software
Agentic workflow automation
We build AI agents that can retrieve data, prepare documents, compare records, generate drafts, and start workflows in ERP, CRM, logistics, HR, and finance systems. Human approval can stay in the loop for financial, legal, medical, or customer-facing actions.
Permission-aware company knowledge access
A company AI assistant should not expose HR, financial, legal, or customer data to employees who cannot access it in the source system. We design RAG pipelines that check the user's corporate identity before retrieving documents. The assistant can only use the data that the employee is allowed to view.
Data privacy and zero-retention-ready architecture
For sensitive use cases, we design architectures that limit what leaves your environment. This can include Azure OpenAI private networking, provider-level data controls, local PII redaction, encrypted storage, audit logging, and self-hosted LLM deployment. The exact setup depends on your compliance needs and the provider terms selected for the project.
Lower operational cost through LLMOps
LLM costs can rise quickly when every user request goes straight to the most expensive model. We add model routing, semantic caching, token budgets, prompt compression, context trimming, and usage dashboards. Your team gets more control over API spend without removing the AI features users need.
Better answers from governed data pipelines
A useful LLM application depends on the data pipeline behind it. We prepare enterprise knowledge for retrieval by cleaning documents, structuring metadata, splitting content into meaningful chunks, embedding it into a vector database, and testing retrieval quality. This gives the model better context and reduces unsupported answers.
Safer AI behavior in production
Enterprise AI needs boundaries around data, actions, and output. We add guardrails for prompt injection, sensitive data exposure, excessive tool access, invalid output, and unsupported claims. The system is tested before launch and monitored after deployment.
Have a vision for an AI-powered app? Our expert developers can bring it to life with OpenAI’s cutting-edge models.
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Agentic blueprints for enterprise use cases
FinTech: compliance and audit copilots
We build RAG-based assistants that retrieve internal policies, regulatory documents, contract clauses, transaction records, and audit notes.
Risk and compliance teams can ask questions across large document sets, compare contract language against internal rules, and prepare review notes with source references. Access controls restrict which records each user can retrieve.
Fintech software development
Logistics and supply chain: autonomous RFQ agents
We build agentic workflows that process inbound vendor emails, extract pricing terms, compare them with ERP data, and draft negotiation responses.
A human reviewer can approve the response before the system sends it or updates the CRM. This keeps procurement teams in control while reducing manual comparison work.
Logistics software development
Healthcare: clinical operations assistants
We build AI assistants for administrative and operational workflows, such as patient intake support, appointment coordination, insurance document processing, and internal knowledge search.
For regulated environments, we design access controls, PII masking, audit logs, and deployment architecture to meet the organization's compliance requirements.
Healthcare software development
Manufacturing: maintenance and operations copilots
We connect LLMs to manuals, machine logs, maintenance records, sensor summaries, and internal procedures.
Engineers can ask questions about equipment behavior, retrieve troubleshooting steps, compare historical incidents, and prepare maintenance notes. The system can suggest next steps while leaving final decisions to the responsible team.
Awards& Recognitions
Nexterse LLC has been recognized by the leading analytics agencies as the top ChatGPT application development company worldwide. Our values and expertise help us provide professional ChatGPT application development services.
Recent software we made
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Our AI headshot platform was growing fast, and our generation pipeline was starting to show it, with turnaround times creeping up whenever demand spiked and quality consistency becoming harder to guarantee at volume. Nexterse LLC rebuilt our image pipeline around a more resilient queuing and processing architecture, so thousands of concurrent headshot jobs no longer competed for the same resources. They also tightened how we handle and discard uploaded photos, which mattered a lot given how sensitive that data is. Turnaround time dropped, output stayed consistent even during our biggest traffic days, and we've been able to scale well past a million headshots delivered without the platform buckling.
As we grew from one AI photography product to a small family of them, our engineering team was stretched thin trying to keep every product's infrastructure reliable at the same time. Nexterse LLC came in as an extension of our engineering team and helped us standardize the infrastructure across our products, so improvements to one no longer meant reinventing the wheel for another. Deploys became safer, incident response got faster, and our small team could finally focus on product instead of firefighting. It's the kind of partner that actually understands what it means to build fast without breaking things.
Our skills intelligence platform runs on a stack of proprietary language models, and as enterprise customers scaled up their usage, keeping inference fast and accurate across every model became a serious infrastructure challenge. Nexterse LLC helped us optimize how our models are served and monitored in production, cutting inference latency significantly while keeping accuracy where our enterprise customers need it. That work gave us the headroom to keep growing without our infrastructure becoming the bottleneck, and it's held up well through some of our fastest growth to date.
We set out to build a QA platform that could learn how real users move through a product and keep testing those flows on its own, but getting that kind of autonomous testing to be reliable enough for teams to actually trust was the hard part. Nexterse LLC worked with us on the engine that captures and replays user flows, helping us cut down on flaky test runs and false failures that would have killed trust in the product early on. The platform now catches real regressions before they reach users, consistently, which is the entire point of what we set out to build.
Our research produces genuinely more efficient language models, but turning that research into a product that customers could actually integrate and rely on was a different kind of problem than the one we're used to solving. Nexterse LLC helped us build the serving and integration layer around our models, so customers get a stable API and predictable performance instead of having to understand the research underneath it. That layer has made it far easier for us to get our efficiency gains in front of customers without asking them to compromise on reliability.
Running a model agency with a roster of thousands means an enormous amount of profiles, bookings, and digital assets to keep organized, and our internal tools hadn't kept pace with how the industry was moving toward digital modeling. Nexterse LLC built us a platform to manage our models' profiles, availability, and digital assets in one place, and helped us lay the technical groundwork for offering digital twins of our models to brands. What used to be scattered across spreadsheets and inboxes is now a single system our whole team relies on daily, and it's opened doors to work we simply couldn't have taken on before.
Our revenue management platform pulls in pricing and demand data from tens of thousands of properties in near real time, and as we scaled, keeping that data pipeline fast and accurate became a real engineering challenge. Nexterse LLC helped us re-architect parts of our data ingestion layer so it could handle far higher throughput without falling behind during peak booking periods. The platform now processes rate and demand signals faster and more reliably, which directly translates into better pricing recommendations for the properties that depend on us. It's exactly the kind of partner you want when the data never stops coming.
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Our ADLC process for ChatGPT and LLM applications
AI feasibility sprint
We start with a 2- to 4-week feasibility sprint when the use case, data quality, or operating costs need proof before full development. Our team reviews the target workflow, samples the data, builds a small RAG or agentic prototype, and estimates token usage, latency, retrieval quality, and implementation risks. You get a working prototype and an architecture blueprint before committing to a full build.
Data discovery and access design
We map the data sources the LLM may use and the systems it may interact with. This includes company documents, databases, CRM records, ERP data, ticket histories, product catalogs, policies, and third-party APIs. We also define user roles, access rules, retention limits, logging requirements, and approval steps.
Vectorization and RAG engineering
We build the retrieval pipeline that turns company knowledge into a searchable context. The work can include OCR, document parsing, semantic chunking, metadata design, embedding generation, vector indexing, re-ranking, and retrieval testing. The LLM receives only the context needed for a given task.
Agentic architecture and tool integration
We design how the LLM will interact with business systems. For assistant use cases, this may mean search and summarization. For agentic workflows, it can include tool calls, API actions, workflow orchestration, human approval gates, rollback logic, and admin controls.
Security guardrails and red-team testing
We test the system against prompt injection, unauthorized data access, unsafe tool calls, sensitive data exposure, and invalid outputs. Then we add controls such as input classifiers, output validators, PII redaction, role-based retrieval, allowlisted tools, and audit trails.
LLMOps deployment
We prepare the application for production use. This includes CI/CD, prompt versioning, evaluation datasets, monitoring dashboards, model fallback rules, token budgets, semantic caching, and incident response procedures.
Continuous evaluation and improvement
After launch, we monitor answer quality, retrieval precision, hallucination risk, latency, cost, and user feedback. When source data, prompts, models, or business rules change, we update the evaluation suite and deployment controls to maintain system stability.
Frequently asked questions
We use retrieval-augmented generation, which means the model receives relevant context from your approved knowledge base before answering. We also add evaluation checks that compare the answer against the retrieved context. For higher-risk use cases, the system can block low-confidence answers, show source references, or route the request to a human reviewer.
Why Nexterse LLC
AI feasibility and strategy sprint
Before writing the core application code, we can run a 2- to 4-week AI feasibility sprint. We take a sample of your enterprise data, build a localized RAG proof of concept, and measure retrieval quality, response accuracy, token cost, latency, and implementation risk. You get a working prototype and an architecture blueprint before the full build.
Data privacy and PII redaction architecture
We design data flows that reduce exposure of sensitive information. For use cases that need additional protection, we add PII redaction middleware before the LLM call. Local models can mask sensitive fields such as financial data, patient names, customer records, and employee identifiers. After the LLM responds, middleware restores the allowed data for authorized users.
AI tech debt rescue
We help teams replace fragile AI prototypes with maintainable software. Our engineers refactor unstructured LangChain scripts, unstable vector searches, unmanaged prompts, and single-provider integrations into production-ready services. The new architecture can include RBAC, monitoring, model routing, caching, CI/CD, and support workflows.
LLMOps and token cost management
We build cost controls into the application architecture. This can include semantic caching with Redis, model routing, token budgets, context trimming, fallback models, and usage dashboards. Repeated or low-risk requests can be routed away from expensive model calls when the architecture allows it.
Dual-Engine engineering approach
Nexterse LLC combines traditional software engineering with the Agentic Development Lifecycle. The SDLC side covers deterministic application logic, APIs, databases, UI, infrastructure, and integrations. The ADLC side covers prompts, RAG, agents, guardrails, model evaluations, red-team testing, and LLMOps.
Enterprise software background
Nexterse LLC has experience building custom software for enterprise workflows, regulated data, legacy integrations, and long-term product support. For LLM projects, this matters because the AI layer still needs stable software architecture, secure deployment, user management, observability, and maintainable code.
Key numbers about Nexterse LLC
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