AI PoC development services that prove ROI in 4 weeks
SumatoSoft develops fixed-scope AI PoCs that deliver a working sandbox prototype, a cost-per-query model, a private deployment blueprint, and a roadmap for the next build. We deliver explicit success metrics, pressure-test guardrails, and production cost runtime models.
AI hype is expensive. We engineered a safer way.
80% of enterprise AI projects never make it to production. We know where they lose momentum. A team assembles 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 realize the “quick PoC” was built with shortcuts that do not belong in a production path.
We do not treat AI proof-of-concept development as a generic discovery phase followed by a loose prototype. We run it within our Agentic Development Lifecycle (ADLC). It’s an AI-governed delivery model in which AI operates within defined boundaries from day one.
ADLC is built around:
- AI as an operational component
- Policy-driven development
- Built-in quality gates
- Guardrail-based automation with explicit decision rules
- Token-cost observability and delivery telemetry
- Human-led, AI-executed workflows
This way, we strengthen SDLC by improving throughput, visibility, and control.
Build vs. Buy vs. SumatoSoft
When a Client says, “We need an AI PoC,” there are usually three paths on the table: buy an off-the-shelf AI tool, ask an agency for a quick pilot, or run a controlled proof of concept built for a real production decision. If you need evidence for a go/no-go decision, the 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. |
Feature
Data privacy
Customization
Financial predictability
IP ownership
Off-the-shelf AI SaaS
Shared vendor environment and limited control over data boundaries
Limited to vendor workflows and roadmap
Per-seat or bundled pricing hides scaling costs.
You do not own the product.
Typical agency “Free PoC”
Often built on public APIs with loose handling of company data
Thin wrapper around an API
No clear usage model for token, retrieval, and infra costs.
Ownership terms are often unclear.
SumatoSoft Pilot & Prove
Private deployment design with controlled access and enterprise-grade boundaries
Custom agentic architecture aligned to your use case and systems.
Cost-per-query model and runtime cost projection based on agreed assumptions.
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 fundamental filter between a costly hypothesis and a profitable reality. It allows businesses to fail fast, learn cheaply, and scale only what is proven to work.
What happens inside the 4-week ADLC sprint
The sprint is transparent by design. Every week has its purpose, outputs, and a role in reducing uncertainty. It is an agent-orchestrated delivery inside a controlled framework.
We define the business problem, the success metric, and the boundaries.
This week includes:
- Aligning on one measurable business outcome
- Defining what the PoC will and will not do
- Identifying data sources and access constraints
- Setting handling rules for sensitive information
- Outlining failure conditions and refusal rules
- Establishing the review path for stakeholders
We map your data environment to the solution path.
This week includes:
- Reviewing source systems, documents, and data quality
- Identifying what can be used now and what needs cleanup
- Selecting the model access path that fits your use case and governance needs
- Defining the retrieval strategy, context strategy, and access model
- Designing 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 includes:
- Agent-driven workflow setup
- Core orchestration for retrieval, reasoning, and tool use
- Structured task chains for the scoped use case
- Response controls and policy gates
- Memory and state handling where needed
- Interface layer for stakeholder review
The system is assembled through controlled, autonomous execution, with validation built into the flow rather than added later.
We stress-test the solution before you make a decision.
This week includes:
- Testing against unsafe outputs, weak retrieval, and broken logic paths
- Pressure-testing prompt handling and output controls
- Reviewing edge cases and governance gaps
- Finalizing the runtime cost model
- Presenting the PoC, architecture blueprint, and 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, economics, or delivery conditions are not strong enough yet, it has done its job. It has prevented 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.
Deliverables: What you get from the AI PoC engagement
Most AI PoC development services stop at “you get a prototype.” That is not enough for a serious buying decision. SumatoSoft’s AI Pilot & Prove program delivers a decision package: a working sandbox build, a cost model you can budget, 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. The prototype is built on a controlled slice of your data or a sanitized dataset. It is designed to determine whether this use case works in your environment under your constraints.
Typical formats include:
Token-cost projection
A cost model that shows what it will cost to run in production under agreed usage assumptions. You get a budget view that shows the cost of changes as usage grows.
It covers cost drivers such as:
Security architecture blueprint
An architecture blueprint for a private, enterprise-grade deployment path. Result: your security team can review controls on paper before anything scales.
It includes:
Production roadmap
A plan that prevents a restart from zero if the PoC is approved.
It outlines:
Executive readout
A structured readout for technical and business stakeholders.
It answers:
Functional sandbox prototype
A working prototype built around one tightly scoped use case. The prototype is built on a controlled slice of your data or a sanitized dataset. It is designed to determine whether this use case works in your environment under your constraints.
Typical formats include:
Token-cost projection
A cost model that shows what it will cost to run in production under agreed usage assumptions. You get a budget view that shows the cost of changes as usage grows.
It covers cost drivers such as:
Security architecture blueprint
An architecture blueprint for a private, enterprise-grade deployment path. Result: your security team can review controls on paper before anything scales.
It includes:
Production roadmap
A plan that prevents a restart from zero if the PoC is approved.
It outlines:
Executive readout
A structured readout for technical and business stakeholders.
It answers:
Your data is your IP. It stays that way.
SumatoSoft’s AI PoC development model is built on security-by-design AI. This means we use controlled access patterns and design the solution for auditability from the start. SumatoSoftis ISO 27001 certified and works in line with regulations, including GDPR and the EU AI Act.
Zero public training
Your proprietary documents and internal data are not treated as training fuel for public models. The solution path is designed around enterprise-safe model access and controlled data handling.
Guardrails before autonomy
We do not hand decision-making to an unconstrained workflow. High-risk actions are handled after review steps, approval logic, or hard stop conditions.
Controlled tool access
Any system that can call an external service, retrieve data, or trigger an internal action must include limits as part of its design.
Traceability and auditability
Inputs, retrieval paths, outputs, and execution decisions should be reviewable. If the system makes a weak recommendation, you need a clear way to understand why.
Budget-constrained AI workflows
We do not let the system run as an open meter. Cost visibility and usage boundaries are part of delivery governance.
You own the output
The code, prompts, architecture, and delivered assets are the property of 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 ADCL?
We do not treat AI proof-of-concept development as a generic discovery phase followed by a loose prototype. We run it within our Agentic Development Lifecycle (ADLC). It’s an AI-governed delivery model in which AI operates within defined boundaries from day one.
ADLC is built around:
AI as an operational component
Policy-driven development
Built-in quality gates
Guardrail-based automation with explicit decision rules
Token-cost observability and delivery telemetry
Human-led, AI-executed workflows
Our tech stack for AI PoC development
We choose tools based on your use case, data sensitivity, performance expectations, and deployment requirements.
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
Our recent AI cases
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 great in every regard including costs, professionalism, transparency, and willingness to guide. I think they were great advisors early on when we weren’t ready with a fully fleshed idea that could go to market.
They know the business and startup scene as well globally.
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.
The 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 can-do attitude, amazing work ethic and willingness to tackle client’s 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 a 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 say with confidence that SumatoSoft has realized all our ideas into practice.
Let’s start
If you have any questions, email us info@sumatosoft.com
Frequently asked questions
How much does the PoC cost?
It sits in the $10,000–$25,000 range with a 2–4 week timeline. It’s a fixed-price engagement. The final price depends on scope, data readiness, integration needs, and the level of evaluation required.
Who owns the IP of the PoC?
You do. The code, prompts, architecture, and delivered materials are the property of your company under the project agreement.
What if the PoC fails?
Then the PoC may have saved you from a bigger mistake. A strong proof of concept does not exist to force a “yes.” It exists to tell the truth early. If the sprint shows that the data is not ready, the economics do not 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.
Do you need all of our data upfront?
No. In many cases, the first sprint should use a controlled subset of data, a sanitized dataset, or a narrower document domain. The goal is to answer the feasibility question without creating unnecessary exposure.
Can this run inside our environment?
Yes. The deployment path is defined around your governance needs, hosting standards, and access requirements. The exact setup depends on your current infrastructure and security policies.
