Custom AI Software Development Services
SumatoSoft is a custom AI development company that builds RAG systems, copilots, AI agents and private models on top of the software you already run. Projects start with a 4-week proof of concept, and a production build takes three to nine months. We have shipped software for 14+ years across 350+ projects.
What AI services does SumatoSoft offer, by ROI tier?
The right AI tier depends on where you stand today. Budget, data readiness, compliance exposure and operational complexity all shape it. We organize custom AI development into four tiers and weigh the risk, the return and whether each step is feasible in production. For forecasting, anomaly detection and other predictive work, see our machine learning development services.
Tier 1: AI readiness & consulting
We check whether AI pays off for your use case before you spend on development. Our AI readiness assessment audits data quality, infrastructure and integration limits, security and compliance exposure, workflow impact, and projected token and cloud costs.
Tier 2: RAG systems & copilots
This is where most companies start production AI. We build retrieval-augmented generation (RAG) systems and copilots on your documents, ERP, CRM and knowledge bases. They run in an isolated cloud network (VPC) and answer only from verified sources. Each answer cites its source, and access is role-based at the data layer. More on our RAG development services.
Tier 3: Agentic workflows
For processes that cross departments. Our multi-agent systems retrieve data, apply your business rules, call APIs and trigger downstream actions. When confidence drops too low, they hand it off to a person. Every workflow passes evaluation pipelines, adversarial testing and cost simulations before it goes live. More on our AI agent development services.
Tier 4: Custom AI models development
For organizations with large data volumes or heavy regulation. We fine-tune small and large language models (SLMs and LLMs) on your domain, host them privately on AWS, Azure or on-premise, route between models and optimize token costs. See LLM development and fine-tuning.
AI that delivers business value
Contact us for an AI roadmap built around your use case.
How do AI projects start at SumatoSoft?
Most projects start with a 4-week AI proof of concept on a controlled slice of your real data. It ends with a working prototype, a cost-per-query model, a private deployment blueprint and a go/no-go readout for your leadership. Not sure AI fits your case yet? Start with an AI readiness assessment.
What AI has SumatoSoft built?
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.
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.
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.
SumatoSoft succeeded in building a more manageable solution that is much easier to maintain. Their project management reflects the excellence of a larger company. Their team delivers high-quality results, attentive service, and reasonable pricing.
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
Why SumatoSoft: our pragmatic guarantees
We bring engineering discipline and commercial sense to every engagement. We don’t treat AI as a cure-all. We look at your architecture, your data, your risk, and your costs first. Then we tell you where AI belongs and, just as honestly, where it doesn’t.
We will NOT bolt an LLM directly to your core database.
We design secure middleware and API abstraction layers that shield your legacy systems from instability, latency, and injection attacks.
We will NOT use your proprietary data to train public models.
Your data stays inside your own controlled infrastructure. We deploy AI in secure, isolated cloud (VPC) environments with strict access controls and full auditability.
We will NOT push AI where it does not create business value.
If a deterministic, traditional build gets you the result faster and cheaper, that is what we recommend. We are dual-engine engineers. We build AI that earns a return and structured software that keeps things stable.
Can you add AI to our legacy systems?
AI works best on top of solid software, not in place of it. We take the systems you’ve built up over the years and add an intelligent layer on top, without tearing out what already works. We can do this because we’ve shipped traditional software for over 14 years. That makes us a dual-engine firm, with classic engineering and modern AI under one roof.
How does SumatoSoft engineer production AI? (ADLC)
Traditional software follows deterministic logic: meet a condition, and a predefined action runs. AI systems work differently. They generate responses from probability, context, and learned patterns. That difference calls for its own engineering discipline, the Agentic development lifecycle (ADLC).
We define the business goal before we pick a single model. Together we pin down the workflow you want to improve, the outcome you’ll measure it against, and the decision the AI will support. Then we map your data. We find where the knowledge lives, how it moves between teams, and where we’ll need to connect structured and unstructured sources. From day one, the system aims at a target you’ve defined.
A probabilistic system needs hard boundaries, which we call guardrails. We decide which sources it can draw on, how it reaches your data, who is allowed to see what, and when it should refuse to answer at all. These rules aren’t bolted on afterward. We build them into the architecture itself, and it plugs into your ERP, CRM, data warehouses, and internal tools, with full logging and an audit trail throughout.
We measure how the system behaves before anyone outside the test group touches it. Our evaluation pipelines, including frameworks built for retrieval-augmented generation (RAG), check whether answers stay faithful to the source, whether retrieval lands on the right material, and whether the system responds consistently and accurately against the thresholds we set. Nothing scales until the numbers clear the bar. Our QA team also runs AI testing and LLM evaluation as a separate service.
Production brings scale, simultaneous users, and edge cases the test environment never sees. To stay ahead of them, we attack the system on purpose, red-teaming it and simulating prompt-injection attempts to find where it breaks. We confirm it holds up under load, that each interaction costs what we projected, that it doesn’t disrupt neighboring systems, and that it behaves predictably no matter how users phrase their requests. Only once it proves reliable under real conditions does it move to production.
Going live doesn’t end the oversight. We keep watching answer quality, how current the data stays, how people actually use the system, and what it costs to run. When we adjust it, we document every change and tie it back to where your business is headed. The system improves on purpose, and you can measure that improvement.
How does SumatoSoft secure enterprise AI?
AI systems have to run inside clear technical, legal, and operational boundaries. We build those boundaries straight into the architecture rather than adding them later.
Infrastructure-level security
We deploy AI inside your own controlled cloud, AWS or Azure, using private networking, isolated workloads, and encrypted data flows. Access runs on fine-grained, role-based permissions that match your internal policies.
Data governance by design
We decide how data is taken in, processed, stored, and accessed before any model integration starts. The system can redact or mask sensitive information automatically, and it enforces access policies at both the application and retrieval levels.
Compliance alignment
Our delivery processes follow ISO 27001 and support regulations such as GDPR and the EU AI Act where they apply. We build documentation, audit trails, and model-lifecycle transparency into the development process itself.
Operational oversight
We build in monitoring, logging, and performance tracking from day one, so you can trace model outputs, system behavior, and infrastructure usage across every environment.
Which industries does SumatoSoft build AI for?
We build custom AI for the industries below, for both new and established businesses. The work spans big-data analysis, AI development and machine learning.
Retail
- personalized product recommendations;
- inventory forecasting and management;
- dynamic pricing optimization;
- customer behavior analytics;
- AI-powered virtual assistants and chatbots.
Since 2020, AI has gone from an emerging trend to a driving force. What used to be experimental is now essential, reshaping how businesses operate, compete, and grow. At SumatoSoft we’ve leaned into that shift and grown our expertise to help companies put AI to full use.
What’s in SumatoSoft’s AI tech stack?
These are some of the tools we use for AI software development. The final choice depends on your business goals.
| Category | Tools |
|---|---|
Foundation models |
Azure OpenAI, Amazon Bedrock, Anthropic Claude, Meta Llama, Mistral AI |
Orchestration and agents |
LangChain, LlamaIndex, AutoGen, CrewAI |
Enterprise memory (vector databases) |
Milvus, pgvector, Pinecone, Weaviate |
Data processing and multi-modal |
Unstructured, Tesseract, OpenCV, Apache Tika |
LLMOps and evaluation |
PromptLayer, RAGAS, Weights and Biases, LangSmith |
Cloud and infrastructure |
Microsoft Azure, AWS, Kubernetes, Docker |
Awards & recognitions
Your data never trains public models
Enterprise AI needs a clear architecture. Your proprietary information stays fully under your control at every stage of development and deployment.
VPC-isolated deployment architecture
Your AI runs inside your own cloud, AWS or Azure, walled off with VPC isolation, private subnets, security groups, and IAM policies. The models live inside your security perimeter and reach your internal systems through controlled middleware, never by touching your database directly.
Vector-level role-based access control (RBAC)
We control access at the retrieval layer. Role- and attribute-based rules (RBAC and ABAC) mean a user can only pull the data they’re cleared to see, and we check those permissions before the system retrieves anything or writes a word.
Automated PII redaction pipeline
Before any sensitive data gets indexed or reaches a model, it runs through an automated pipeline that finds and redacts personal information (PII). We use entity recognition, masking, and tokenization to keep protected data out of layers it was never meant to reach.
Private model invocation and secure API mediation
We reach foundation models either through secure API gateways or through private endpoints we deploy for you. Every call is logged and rate-limited, and middleware inside your own infrastructure governs all of it.
Audit logging and access traceability
We log every interaction, each retrieval, each generated answer, and each system call, so you can trace any of them later. The audit trail gives you operational transparency and the records your compliance team needs.
Frequently asked questions
What services does SumatoSoft offer for AI software development?
SumatoSoft offers four AI service lines: AI readiness and consulting, RAG systems and copilots, agentic workflows, and custom AI model development. You can start with an AI readiness assessment, build on your own knowledge with RAG development services, automate cross-team work with AI agent development services, or run private models with LLM development and fine-tuning.
What does a custom AI development company do?
A custom AI development company builds AI systems around your own data and software instead of selling a ready-made tool. At SumatoSoft that means checking feasibility, building RAG systems, copilots, agents or private models, and connecting them to your existing systems through secure middleware. Every system is evaluated before release and monitored after it.
Should we build custom AI or buy an off-the-shelf AI tool?
Buy when an off-the-shelf tool covers the workflow and its terms allow your data to leave your environment. Build when the AI has to use your own systems and documents, follow your access rules, stay inside your cloud, or support a process no tool covers. A 4-week AI proof of concept tests the build option on your data before you commit.
How do I choose a custom AI development company?
Check three things. First, production AI work you can verify in published case studies. Second, experience integrating AI with software like yours, not only building demos. Third, a defined process for evaluation, security and cost control. Ask for a fixed-scope proof of concept with measured accuracy and a token-cost forecast before you sign a full build.
What is the difference between custom AI development and generative AI development?
Generative AI development is one part of custom AI development. It covers systems that generate text, code or images, such as copilots and RAG assistants. Custom AI development also covers predictive models, computer vision and agentic workflows that act across your systems. See our generative AI development services.
Let’s start
If you have any questions, email us info@sumatosoft.com
