SumatoSoft

We're web & mobile application developers team delivering the business-oriented solutions. Find out What was the idea behind starting this organization?

Please introduce your company and give a brief about your role within the company? SumatoSoft is a custom software development company that focuses on turn-key projects, which means we provide a full range of services, from business analysis and software prototyping to UX design, development, quality assurance and support. At SumatoSoft, we strive to apply cutting-edge technology along with our co

mprehensive experience, dedication and technical expertise to make sure our customers get solutions that fully meet their business needs. Being a co-founder, not only am I responsible for SumatoSoft’s global strategy but also I am directly involved in managing resources, building and maintaining lasting relationships with customers. Together with Vladimir Shidlovsky, my business partner and co-founder of SumatoSoft, we decided to set up a company that would provide businesses with high-quality custom software and what is more, supports our clients throughout a whole way of their ideas’ implementation. We made Quality the cornerstone of all the company’s activities and adhere to the concept to this day, which allows SumatoSoft to become a technology partner for our customers. What are your company’s business model–in-house team or third party vendors/ outsourcing? We are a dedicated in-house team running like clockwork where everyone plays an important role and contributes to the success of each project and company as a whole. I am proud to say we brought together true masters of their craft. How is your business model beneficial from a value added perspective to the clients compared to other companies' models? Thanks to the path we took, we have managed to provide full transparency to our customers at each stage of our cooperation. SumatoSoft’s clients are able to meet and talk to each team member personally. Other benefits are the team coherence and high development speed, as the team is co-located. What industries do you generally cater to? Are your customers repetitive? If yes, what ratio of clients has been repetitive to you? We have a proven track record of delivering high-quality automotive IT solutions, IoT products, marketing automation & e-commerce software, applications for logistics, to name a few. In general, with adequate resources and a high expertise level, we are able to provide solutions to business challenges in any domain. Speaking about our clients, about 90-95 percent of them are repetitive: once a client has an idea for a new product or service, they turn to the proven technology partner – SumatoSoft. What is more, many businesses come to us on the recommendations of our existing customers.

06/02/2026

How small companies fine-tune without proprietary training data 🧠

A common assumption that's no longer correct: you need a large proprietary dataset to fine-tune a model usefully.

For most enterprise fine-tuning use cases in 2026, including domain-specific tone, structured output adherence, workflow-specific reasoning, and brand voice consistency, synthetic data has become a first-class option. The pattern works like this: you describe what you want the model to do in detail, you use a strong frontier model to generate hundreds or thousands of input-output pairs that match the description, you filter the synthetic pairs for quality, and you fine-tune a smaller model on the result. 🔄

The fine-tuned smaller model often performs the target task as well as the frontier model, at a fraction of the inference cost. This is the technique behind several recent open-weight model releases that punch above their parameter count.💰

Where synthetic data does and doesn't work: it works for behavioural fine-tuning (tone, format, structured output, domain-vocabulary adoption). It works less well for fine-tuning on specialised factual knowledge, where you want the model to know things it didn't know before. For factual grounding, RAG or graph RAG remains the better tool. The two approaches combine well: fine-tune for behaviour, retrieve for facts.🧩

For companies that have been told "we can't do AI because we don't have enough data," this changes the conversation. The data constraint that blocked fine-tuning in 2023 has loosened. What you need now is a clear specification of what the model should do, a frontier-model budget for synthetic generation, an eval suite to verify the result, and a serving infrastructure for the fine-tuned model. The fine-tuned model that comes out can often run on your own infrastructure for a few hundred dollars a month.✅

05/29/2026

Voice AI just had its plumbing year 🎙️

Something happened to voice AI in the last twelve months that made it production-grade for enterprise deployments. 🏭

The core capability of speech-to-text plus LLM plus text-to-speech has existed for several years. What changed is the orchestration layer between them. Latency dropped from 3 to 4 seconds to under 800 milliseconds for a full conversational turn. Interruption handling, the thing that made voice agents sound robotic, became standard. Tone and emotion in synthesised speech crossed the uncanny valley for most use cases. Voice infrastructure platforms such as Vapi, Retell, and LiveKit shipped the production tooling that turns these capabilities into a deployable service. ⚙️📉

The deployment patterns we see this year: customer service IVR replacement (the largest category, with carrier-to-carrier check calls being a logistics-specific example), outbound qualifying calls for sales operations, appointment scheduling and confirmation, and field-service follow-up calls. None of these require frontier reasoning. They require reliable conversational flow under cost and latency budgets that match human-call economics. 📞✅

The question worth answering before scoping a voice project: which calls in your operation follow a script with predictable variations? Those calls are now automatable. The calls that don't follow scripts (relationship management, complex troubleshooting, anything where tone and judgment carry the conversation) remain human work for now. The economics of voice AI deployments only land when you sort calls correctly into the two buckets. 🧠🗂️

05/26/2026

The missing layer in most enterprise AI stacks 🧩

A gap I see in most enterprise AI deployments: the application is in production, the model is in production, and the observability layer is missing. 👀

In a normal software stack, observability is non-negotiable. Logs, metrics, traces, alerts. You wouldn't ship a service without them. AI features get shipped without their equivalent more often than not, and the consequences arrive at month four. 📉

What AI observability covers, beyond traditional APM: request-level traces of the model call (system prompt, retrieved context, model output, latency, token cost), eval results in production rather than only at deploy, drift detection on input distribution and output quality, cost tracking per workflow and per tenant, and audit logs of which version of which prompt produced which decision. 🧠

The tools have matured. LangSmith, Langfuse, Arize, Helicone, and Datadog's LLM observability layer all hit production-ready in the last 18 months. 🛠️

What hasn't matured is the procurement reflex to include them. Most AI vendor proposals quote the model integration and skip the observability layer. The buyer doesn't notice until production, by which point retrofitting observability into a deployed system costs more than building it in. ⏳

05/21/2026

When plain RAG breaks ⚙️

A pattern across enterprise AI projects this year: vanilla RAG works for the first 80 percent of queries and breaks on the next 15. The break is structural, and a class of fixes called "graph RAG" has emerged to handle it. 📉

Plain retrieval-augmented generation works by chunking your documents, embedding the chunks, and retrieving the closest chunks to the query. This works when the answer to a query lives in a single document or a handful of contiguous chunks. It breaks when the answer requires connecting facts across documents that don't sit close together in embedding space. 🔗❌

The classic example: "Which of our suppliers have had compliance incidents and also serve customers in regulated industries?" That answer requires a multi-hop traversal. Suppliers, then incidents, then suppliers again, then customers, then customers' industries. No chunk contains the full answer. Embedding similarity won't find it. 🧩

Graph RAG approaches solve this by building a knowledge graph from the source documents (entities, relationships, attributes), then querying the graph alongside or instead of the embedding store. Microsoft published a notable graph RAG paper last year, and several open-source frameworks now implement the pattern. Production deployments are landing across legal, healthcare, supply chain, and financial services in 2026. 📊✅

05/19/2026

"Prompt engineering" is a 2023 word 📅

The phrase has quietly stopped showing up in serious AI engineering practice. What replaced it: context engineering. 🔄

Prompt engineering was about phrasing the instruction well: writing a single prompt that produces a good output for a single use. Context engineering is about assembling everything the model needs to do its job. The system prompt, retrieved documents, tool definitions, conversation history, user metadata, structured examples, and policy constraints, all selected and ordered for the specific request. 🧠

For a single-shot question, the difference is invisible. For an agent handling a complex workflow, it determines whether the system works at all. The agent that has access to all the right context produces good outputs reliably. The same agent with everything except the right ordering produces unpredictable outputs that look like model failures but are context-assembly failures. ⚙️

What this means for projects: the engineering work is upstream of the prompt. Building the retrieval pipeline that finds relevant content. Building the metadata layer that injects user-specific context. Building the templating system that orders information for the model's attention pattern. Building the cache layer that makes context-heavy requests affordable. Most of the work that determines whether an AI feature ships well lives in assembly, not phrasing. 🏗️

When a vendor pitches "we'll write your prompts," they're selling 2023. When they pitch context architecture, schema design for agent inputs, and retrieval evaluation, they're selling what 2026 ships. 📦

05/14/2026

If your AI deployment doesn't run evals on every commit, it isn't in production ⚙️

A pattern across AI integration projects that ship versus ones that don't: the shipping ones treat evaluations as production infrastructure, not as a testing phase. 🚢

The distinction is straightforward. In a normal software stack, you write unit tests, integration tests, and end-to-end tests. They run on every commit. If they fail, the deploy is blocked. AI features need an equivalent layer, and the equivalent is the eval suite. A test answers "did the function return the expected output?" An eval answers "did the model produce a response of acceptable quality on a representative input?" 🤖

What an eval suite for a production AI feature looks like: a fixed set of inputs that span the workflow's distribution, ground-truth answers or scoring rubrics, automated grading (often by another LLM or a deterministic check), latency and cost gates, and regression detection across versions. Anthropic, OpenAI, and Google all publish their internal eval setups now. The pattern has converged. 📊

Why this matters for the buyer: when a vendor says they tested the AI feature, the question to ask is "do you run evals on every deploy?" Vendors that say yes are operating in 2026. The ones that conflate testing with evals are operating in 2023, and you'll find out in production. 🛒

Most failed AI integration projects didn't fail because the model was wrong. They failed because nobody had a running eval suite that would have caught the regression when the prompt changed, the model version updated, or the retrieval source drifted. Build the eval suite first. The model work is downstream of it. 🔧

05/12/2026

How AI agents talk to each other (and why MCP wasn't enough) 🗣️🤖

MCP solved how an AI agent calls a tool. A separate problem has been getting attention in 2025 and 2026: how AI agents work with other AI agents. The protocol that's emerging here is called A2A, originally proposed by Google and now joined by Cisco, ServiceNow, SAP, and a growing list of enterprise vendors. 🔄

The 2026 deployments increasingly involve several agents collaborating: a planner agent that breaks down a goal, executor agents that handle individual steps, and a verifier agent that checks the output before it commits. That topology needs a protocol. 🧠⚙️

What A2A specifies: discovery (one agent finding others), capability advertisement (what an agent can do), task negotiation (handing off work), and outcome reporting (telling the requester what happened). It sits one layer above MCP, which deals with agent-to-tool calls. 📡

For most enterprises this isn't urgent yet. Single-agent deployments are still where the bulk of production AI lives. The architectural decision worth making now is whether your agent framework supports A2A as it matures, or whether you're betting on a vendor's proprietary multi-agent layer that locks you in the same way orchestration layers do. 🔒🏗️

05/08/2026

Where AI vendor lock-in lives 🔒

A Docker survey published this month found that 76-81% of enterprises are concerned about vendor lock-in in their agentic AI deployments. The number is striking. The placement of the lock-in is even more striking, and most procurement teams are looking in the wrong place. 🎯

When companies evaluate AI vendors, attention concentrates on the model: GPT versus Claude versus Gemini, capabilities, and pricing per token. This is the most visible layer and also the easiest to swap. Models are increasingly commodity-like, with Stanford's 2026 AI Index showing the top frontier models separated by razor-thin margins. 🤖🔄

Lock-in sits one layer below, at the orchestration and memory layer, where vendors build their proprietary surface. How agents persist state between sessions. How they pass context to one another in multi-agent workflows. How retrieval and grounding are configured against your data. How permissions and audit trails are stored. Migrate the model, and these survive. Migrate the orchestration platform, and most of this gets rebuilt from scratch. Orchestration vendors are happy to be flexible about which model you use, precisely because the model is not where they have you. 🧠⚙️

The architectural choice that determines your lock-in exposure five years from now is not which LLM you sign with this quarter. It is whether your orchestration, memory, and retrieval layers run on open standards such as MCP, A2A, and vector stores you control, or on a vendor's proprietary stack. 🏗️

We have started explicitly writing this question into our discovery process. Most clients have not thought about it because no one in the sales conversation is incentivized to raise it. If you are signing an AI platform contract this quarter, the most important paragraph in the document is the one describing data and state portability at exit. Read it twice. 📄👀

05/06/2026

What MCP means for your enterprise AI stack 🔌

Lucidworks released an MCP server three weeks ago, claiming reductions of up to 10x in enterprise AI integration timelines and savings of over $150,000 per integration. Vendor-reported numbers deserve scrutiny, but the underlying shift is worth understanding regardless of how generous Lucidworks's calculator is. 📊

Model Context Protocol is an open standard, originally developed by Anthropic and now stewarded by the Linux Foundation, that defines how AI models discover and call external tools. It is running on more than 10,000 public servers as of this month, with adoption from OpenAI, Google, Microsoft, and AWS. The protocol is on track to become the universal interface between AI agents and enterprise systems. 🔧
For most companies, several things follow from this. The "let's build a custom integration so our chatbot can read from Salesforce" project that you scoped six months ago has become substantially smaller, and a vendor that is not using MCP should be asked why. ❓ The economics of multi-system agents shift in the same direction. Most agentic deployments stall because connecting an agent to five enterprise systems used to require five custom adapters with five different security models, but MCP collapses that into a single pattern. 🔄

New attack surface is a counterweight worth taking seriously. Asana had an MCP-related tenant-isolation flaw earlier this year that affected up to 1,000 enterprises, and WordPress plugins exposed more than 100,000 sites. The standard arrived faster than the security tooling around it, so MCP rollouts in the next two quarters need security reviews scoped accordingly. 🔒

We have moved our AI integration practice to MCP-first as the default architecture, with custom adapters reserved for legacy systems that cannot expose what MCP needs. 🏗️ If you are scoping AI agent work this quarter, this is the architectural decision that will look obvious in eighteen months and expensive to undo. ⏳

04/30/2026

What affects AI development cost in 2026 💰

AI budgets are shaped by more than model choice.
In our latest article, we break down the main drivers: project complexity, data quality and labeling, privacy and compliance needs, infrastructure choices, third-party model usage, and ongoing spend after launch. 🔧
The article also notes that simple AI systems can start at 5000 to 50000, while enterprise-grade solutions can reach 400000 to 1M+, with ongoing costs often adding 17 to 30% per year. 📈

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