AI PoC development services that prove ROI in 4 weeks
SumatoSoft builds fixed-scope AI proofs of concept (PoCs). Each one gives you a working sandbox prototype, a cost-per-query model, a private deployment blueprint, and a roadmap for the next build. We set explicit success metrics, pressure-test the guardrails, and model what the system will cost to run in production.
AI hype is expensive. We engineered a safer way.
80% of enterprise AI projects never reach production, and we know where they stall. A team throws together a quick prototype, stakeholders get interested, and then the friction starts. Security teams question the data flow. Finance teams ask what token usage, infrastructure, and monitoring will cost at scale. Delivery teams discover the “quick PoC” was built with shortcuts that have no place in a production path.
We don’t treat an AI proof of concept as a vague discovery phase followed by a loose prototype. We run it within our Agentic Development Lifecycle (ADLC), a delivery model where AI works within defined boundaries from day one.
ADLC is built around six ideas.
- AI is an operational component.
- Development is policy-driven.
- Quality gates are built in.
- Automation follows guardrails with explicit decision rules.
- Token costs and delivery telemetry stay observable.
- Workflows are human-led but AI-executed.
Together these strengthen the traditional software lifecycle (SDLC) by improving throughput, visibility, and control.
What happens inside the 4-week ADLC sprint
The sprint is transparent by design. Every week has its own purpose, its own outputs, and a clear part to play in reducing uncertainty. Agents run the delivery, inside a controlled framework.
We define the business problem, the success metric, and the boundaries. This week
- We agree on one measurable business outcome.
- We define what the PoC will and will not do.
- We identify data sources and access constraints.
- We set handling rules for sensitive information.
- We outline failure conditions and refusal rules.
- We also establish the review path for stakeholders.
We map your data environment onto the solution path. This week
- We review source systems, documents, and data quality.
- We identify what we can use now and what needs cleanup.
- We select the model access path that fits your use case and governance needs.
- We define the retrieval, context, and access strategies.
- We design the deployment path for a private or tightly controlled environment.
This is where the system stops being “an idea for AI” and becomes a defined architecture.
We build the proof of concept in a controlled environment, using human-led, AI-executed workflows. This week
- We set up the agent-driven workflow.
- We handle core orchestration for retrieval, reasoning, and tool use.
- We build structured task chains for the scoped use case.
- We add response controls and policy gates.
- We handle memory and state where needed.
- We add an interface layer for stakeholder review.
The system comes together through controlled, autonomous execution, with validation built into the flow rather than bolted on later.
We stress-test the solution before you make a decision. This week
- We test against unsafe outputs, weak retrieval, and broken logic paths.
- We pressure-test prompt handling and output controls.
- We review edge cases and governance gaps.
- We finalize the runtime cost model.
- We present the PoC, the architecture blueprint, and the production roadmap.
The sprint ends with your decision to proceed, refine, or stop.
A stopped PoC is not a failed engagement. If the sprint shows that the data, the economics, or the delivery conditions aren’t strong enough yet, it has done its job. It has saved you from a bigger mistake.
Book your free AI discovery call
Discuss your business challenge with our AI experts and find out exactly how a PoC can solve it.
Build vs. Buy vs. SumatoSoft
When a client says “we need full AI software development services,” there are usually four paths on the table. You can buy an off-the-shelf AI tool, ask an agency for a quick pilot, run a controlled proof of concept built for a real production decision, or take an AI readiness assessment first. If you need evidence for a go/no-go decision, those options narrow fast.
| Feature | Off-the-shelf AI SaaS | Typical agency “Free PoC” | SumatoSoft Pilot & Prove |
|---|---|---|---|
Data privacy |
Shared vendor environment and limited control over data boundaries |
Often built on public APIs with loose handling of company data |
Private deployment design with controlled access and enterprise-grade boundaries |
Customization |
Limited to vendor workflows and roadmap |
Thin wrapper around an API |
Custom agentic architecture aligned to your use case and systems. |
Financial predictability |
Per-seat or bundled pricing hides scaling costs. |
No clear usage model for token, retrieval, and infra costs. |
Cost-per-query model and runtime cost projection based on agreed assumptions. |
IP ownership |
You do not own the product. |
Ownership terms are often unclear. |
You own the code, prompts, architecture, and delivered assets. |
In AI development, a proof of concept is not an optional stepping stone. It is the filter between a costly hypothesis and a profitable reality. It lets a business fail fast, learn cheaply, and scale only what has proven it works.
What AI PoCs has SumatoSoft delivered?
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.
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.
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.
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
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.
From the early stages of the project, SumatoSoft demonstrated a proactive attitude, actively seeking opportunities to enhance the solution and anticipate our needs. They consistently took the initiative to address any potential issues, provide timely updates, and offer solutions to challenges that arose during development. This proactiveness greatly contributed to the project’s success and exceeded our expectations.
Working with SumatoSoft has been an outstanding experience. Their team is not only highly skilled but also incredibly responsive, collaborative, and committed to delivering quality results. I can’t recommend them enough! Thank you team SumatoSoft for bringing my vision to life.
We brought in SumatoSoft to help us reduce unexpected turbine failures, and the result met our expectations.
SumatoSoft is flexible, efficient, and extremely good at planning and being proactive. They have also been very proactive in their approach throughout the project, seeking to understand the needs and the reasons behind them before launching into development, which has been helpful for maintaining direction and consistency, especially because the end client is regularly generating new ideas for added features.
Awards & Recognitions
What do you get from the AI PoC?
Most AI PoC services stop at “you get a prototype,” which is not enough for a serious buying decision. SumatoSoft’s AI Pilot & Prove program delivers a full decision package instead. You get a working sandbox build, a cost model you can budget against, a security blueprint your team can review, and a clear plan for the next build.
Functional sandbox prototype
A working prototype built around one tightly scoped use case. We build it on a controlled slice of your data, or on a sanitized dataset, to find out whether the use case works in your environment and under your constraints.
Typical formats include:
Token-cost projection
A cost model that shows what the system will cost to run in production under agreed usage assumptions. You also get a budget view of how those costs change as usage grows. It covers the real cost drivers such as:
Security architecture blueprint
An architecture blueprint for a private, enterprise-grade deployment path, so your security team can review the controls on paper before anything scales.
It includes:
Production roadmap
A plan that keeps you from starting over if the PoC is approved.
It outlines:
Executive readout
A structured readout for both technical and business stakeholders.
It answers:
Your data is your IP. It stays that way.
SumatoSoft’s AI PoC model is built on security-by-design AI. In practice, that means we use controlled access patterns and design the solution to be auditable from the start. SumatoSoft is ISO 27001 certified and works in line with regulations, including GDPR and the EU AI Act.
Zero public training
We never treat your proprietary documents and internal data as training fuel for public models. We design the solution path around enterprise-safe model access and controlled data handling.
Guardrails before autonomy
We never hand decision-making to an unconstrained workflow. High-risk actions only run after review steps, approval logic, or hard stop conditions.
Controlled tool access
If a system can call an external service, retrieve data, or trigger an internal action, those limits are built into its design, not added afterward.
Traceability and auditability
You should be able to review the inputs, the retrieval paths, the outputs, and the execution decisions. When the system makes a weak recommendation, you need a clear way to see why.
Budget-constrained AI workflows
We never let the system run as an open meter. Cost visibility and usage limits are part of how we govern delivery.
You own the output
The code, the prompts, the architecture, and the delivered assets all belong to your company under the project agreement.
Build your AI PoC in 4 weeks
Accelerate your innovation. Let our team turn your concept into a working model quickly and cost-effectively.
Why SumatoSoft ADLC?
We don’t treat an AI proof of concept as a vague discovery phase followed by a loose prototype. We run it inside our Agentic Development Lifecycle (ADLC), a delivery model where AI works within defined boundaries from day one. ADLC is built around six ideas.
AI is an operational component.
Development is policy-driven.
Quality gates are built in.
Automation follows guardrails with explicit decision rules.
Token costs and delivery telemetry stay observable.
Workflows are human-led but AI-executed.
What’s in the AI PoC tech stack?
We pick tools based on your use case, how sensitive your data is, the performance you expect, and where the solution needs to run.
Foundational models and access paths
- Azure OpenAI
- AWS Bedrock
- Anthropic
- Meta Llama
- and more
Orchestration and agent frameworks
- LangChain
- AutoGen
- LlamaIndex
- CrewAI
- and more
Vector databases and retrieval
- Pinecone
- Weaviate
- pgvector
- Qdrant
- and more
Evaluation, guardrails, and observability
- Response evaluation frameworks
- Logging and traceability layers
- Access control and policy enforcement
- Usage monitoring and budget tracking
More about SumatoSoft
Frequently asked questions
When does a business need an AI PoC?
When the idea is promising but unproven, building it blind would be expensive. A PoC makes sense when you’re unsure the model will hit the accuracy you need. It also helps when leadership wants evidence before funding a full build, or when a vendor’s demo looks great but you don’t know if it holds up on your data. If the approach is already proven for your use case, you can often skip the PoC and go straight to a pilot. We’ll tell you which situation you’re in.
How is an AI PoC different from an MVP or a pilot?
A PoC tests feasibility in a controlled scope. An MVP is a minimal but real product you put in front of users. A pilot runs a validated solution with a limited group before full rollout. They come in that order. Starting with an MVP before feasibility is proven is how budgets get spent on ideas that were never going to work.
How much does an AI PoC cost?
It’s a fixed scope and a fixed price, agreed before we start, so there are no open-ended hours. A 4-week AI PoC typically falls in the low-to-mid five figures. The exact number is set by the complexity of the use case and how ready your data is. You leave with a working prototype, an accuracy report, a cost model, a security blueprint, and a go or no-go recommendation. If the PoC says no, you’ve spent a fraction of a full build to avoid a much larger mistake. For a full-build estimate, use our cost calculator.
Who owns the IP of the PoC?
You do. The code, the prompts, the architecture, and the delivered materials all belong to your company under the project agreement.
What if the PoC fails?
Then it may have saved you from a bigger mistake. A strong proof of concept doesn’t exist to force a “yes.” It exists to tell the truth early. If the sprint shows that your data isn’t ready, the economics don’t hold up, or the use case needs a different architecture, that is still a good outcome. You avoid funding a larger build on weak assumptions, and you leave with a roadmap for what needs to change.
Let’s start
If you have any questions, email us info@sumatosoft.com
