ChatGPT-based Software Development & Integration
SumatoSoft designs custom ChatGPT and LLM-based software for companies that need RAG pipelines, agentic workflows, LLM routing layers, and security guardrails for enterprise-grade AI systems.
- Secure RAG over company data, documents, and business systems
- LLM-agnostic architecture for OpenAI, Claude, Azure-hosted models, and self-hosted LLMs
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.
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.
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.
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.
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 |
Static prompts with limited company context
One model provider hardcoded into the app
Broad access to copied documents
Little visibility into hallucinations
Prompt injection handled only through instructions
Token costs grow with every repeated query
Hard to scale beyond a demo
Dynamic semantic retrieval from approved company sources
Routing layer for OpenAI, Claude, Azure-hosted models, and self-hosted LLMs
Permission-aware retrieval with user-level access checks
Evaluation pipelines that score answer quality against the retrieved context
Input checks, output validation, tool permissions, and audit logs
Token monitoring, caching, batching, and fallback rules
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.
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Agentic blueprints for enterprise use cases
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
Recent software we made
The system has produced a significant competitive advantage in the industry thanks to SumatoSoft’s well-thought opinions.
They shouldered the burden of constantly updating a project management tool with a high level of detail and were committed to producing the best possible solution.
Nectarin LLC aimed to develop a complex Ruby on Rails-based platform, which would be closely integrated with such systems as Google AdWords, Yandex Direct and Google Analytics.
I was impressed by SumatoSoft’s prices, especially for the project I wanted to do and in comparison to the quotes I received from a lot of other companies.
Also, their communication skills were great; it never felt like a long-distance project. It felt like SumatoSoft was working next door because their project manager was always keeping me updated. Initially.
We tried another company that one of our partners had used but they didn’t work out. I feel that SumatoSoft does a better investigation of what we’re asking for. They tell us how they plan to do a task and ask if that works for us. We chose them because their method worked with us.
SumatoSoft is the firm to work with if you want to keep up to high standards. The professional workflows they stick to result in exceptional quality.
Important, they help you think with the business logic of your application and they don’t blindly follow what you are saying. Which is super important. Overall, great skills, good communication, and happy with the results so far.
Together with the team, we have turned the MVP version of the service into a modern full-featured platform for online marketers. We are very satisfied with the work the SumatoSoft team has performed, and we would like to highlight the high level of technical expertise, coherence and efficiency of communication and flexibility in work.
We can confidently say that SumatoSoft has put all our ideas into practice.
We are absolutely convinced that cooperation between companies is only successful when based on effective teamwork (and Captain Obvious is on our side!). But the teams may vary on the degree of their cohesion.
They are very sharp and have a high-quality team. I expect quality from people, and they have the kind of team I can work with. They were upfront about everything that needed to be done.
I appreciated that the cost of the project turned out to be smaller than what we expected because they made some very good suggestions. They are very pleasant to work with.
Rivalfox had the pleasure to work with SumatoSoft in building out core portions of our product, and the results really couldn’t have been better.
SumatoSoft provided us with engineering expertise, enthusiasm and great people that were focused on creating quality features quickly.
We’d like to thank SumatoSoft for the exceptional technical services provided for our business. It should be noted that we started our project’s development with another team, but the communication and the development process in general were not transparent and on schedule. It resulted in a low-quality final product.
SumatoSoft succeeded in building a more manageable solution that is much easier to maintain.
When looking for a strategic IT-partner for the development of a corporate ERP solution, we chose SumatoSoft. The company proved itself a reliable provider of IT services.
Thanks to SumatoSoft’s can-do attitude, amazing work ethic, and willingness to tackle clients’ problems as their own, they’ve become an integral part of our team. We’ve been truly impressed with their professionalism and performance and continue to work with the team on developing new applications.
We are completely satisfied with the results of our cooperation and will be happy to recommend SumatoSoft as a reliable and competent partner for development of web-based solutions
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Our ADLC process for ChatGPT and LLM applications
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.
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.
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.
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.
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.
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.
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.
Things to Know about ChatGPT Development
How do you prevent the LLM from hallucinating when answering questions about our company data?
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.
What happens if an employee tries to access sensitive HR or financial data through the AI copilot?
We build permission-aware retrieval.
The system verifies the employee’s corporate identity using tools such as Okta, Microsoft Entra, or other identity providers. The RAG pipeline retrieves only the documents and records that the user is allowed to access.
How do you reduce prompt injection risk?
We add guardrails before and after the LLM call.
The architecture can include input classification, prompt injection detection, retrieved-context validation, output checks, allowlisted tools, and audit logs. If a request tries to override system instructions or access restricted data, the middleware can block it before it reaches the core workflow.
Do we have to send confidential customer data to OpenAI?
No. The right architecture depends on your compliance needs, provider terms, and deployment requirements.
Options can include Azure OpenAI with enterprise data controls, OpenAI API with configured retention settings, local PII redaction before the LLM call, or self-hosted open-source models deployed inside your infrastructure.
Why do we need RAG if modern models support very large context windows?
Large context windows do not remove the need for retrieval.
Sending long prompts is expensive and can degrade answer quality when relevant information is buried deep within a long input. Research on long-context models found that performance can drop when the relevant information appears in the middle of the context. RAG reduces the prompt to the most relevant passages and helps control cost.
Why SumatoSoft
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
SumatoSoft 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
SumatoSoft 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 SumatoSoft
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If you have any questions, email us info@sumatosoft.com
