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Problem & Needs Analysis
Together, we identify the use cases and business goals where AI can deliver real value.
Companies we work with for this service
Problems we solve
- AI tools selected without a clear business outcome or measurable success criteria.
- Scattered organizational knowledge, manual content processing, and repetitive operations.
- Risks around model quality, safety, cost, and user trust.
- Difficulty moving AI prototypes into existing products and workflows.
Deliverables
- Use-case analysis, data-readiness assessment, and measurable success criteria.
- A focused prototype, model and provider comparison, and technical feasibility findings.
- Product interfaces, service integrations, evaluation workflows, and observability foundations.
- Safety boundaries, usage documentation, and a roadmap for the next iterations.
Our process
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Data & Feasibility Analysis
We assess the available data, systems, and technical requirements to analyze the solution's feasibility.
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Prototype & Development
We validate the solution approach with a PoC, develop it using suitable models and technologies, and integrate it with your existing systems.
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Monitoring & Improvement
We monitor results in production and continuously improve the solution based on performance and evolving needs.
Technologies and tools we use
OpenAI
Python
TypeScript
LangChain
PostgreSQL
Vector Search
Docker
AWS
Featured questions about AI solutions
How long does a project take?
The timeline depends on scope, technical complexity, integrations, design needs, and the speed of feedback. A focused website or design engagement may take a few weeks, an MVP may take a few months, and a complex platform is usually delivered in longer phases. After discovery, we share a realistic plan with milestones, target dates, and dependencies.
How is the cost of software development determined?
Cost is shaped by feature scope, user roles, number of platforms, integrations, data and security requirements, design depth, timeline, and post-launch support. If the scope is clear, we can provide a project-based estimate. If uncertainty is still high, we recommend discovery or a capacity-based model first, so the budget is tied to a prioritized plan rather than assumptions.
How do you ensure privacy and data security?
We treat security as part of the architecture, not as a final checklist. Depending on the product, we plan data minimization, role-based access, encryption in transit and at rest, secure secret management, code review, logging, backups, and dependency updates. We clarify GDPR, KVKK, or organization-specific requirements at the start and limit access to only the people and systems that need it.
Do you provide maintenance and support?
Yes. Maintenance and technical support can cover bug fixes, monitoring, security and dependency updates, performance improvements, small enhancements, and deployment assistance. We define the scope around product criticality, your team’s capabilities, and the response times you need.
How do revisions and iterations work?
We build regular review, demo, and approval points into the project. Feedback is collected in a shared backlog and prioritized by user impact, business value, technical dependencies, and delivery risk. Agreed revision rounds stay within the plan; when a new request changes the scope, we explain its time and budget impact and update the plan after approval.
How do you measure the success of an AI solution?
We define success criteria around the use case at the start. Alongside output accuracy and consistency, we may track task completion, user feedback, latency, operating cost, safety, and the need for human intervention. We compare prototypes against a defined evaluation set, monitor quality changes in production, and prioritize improvements according to measurable outcomes.
Can you take over a project built by another team?
Yes. During technical discovery, we review the codebase, architecture, environments, access, tests, dependencies, release flow, and known issues. We then prepare a takeover plan covering critical risks, technical debt, missing documentation, and quick wins. Before feature work begins, our goal is to gain enough visibility to change the system safely.