AI Integration & Automation
AI features that fit your software.
We can integrate language models, document processing, and knowledge search into new or existing applications. We start with the task, the data, and the limits of the workflow, then decide where AI is useful and what needs a person’s review.
Problems this service solves
A useful feature starts with a specific task.
Information spread across sources
People need answers from internal documents or records, but finding the relevant material takes several searches. Access to each source still needs to follow the user’s permissions.
Documents waiting for attention
Extracting fields, sorting incoming files, or preparing a summary takes time. The workflow needs a way to check uncertain results before anyone relies on them.
A model call without a product workflow
A prototype can produce an answer, yet the application still needs access checks, a usable interface, usage limits, and a way to continue when the provider fails.
WHAT WE CAN BUILD
From a model connection to a working feature.
LLM API integration
Server-side connections to language model APIs, behind authentication and application access rules. We validate structured outputs and define timeout, provider-failure, usage, and cost boundaries around the feature.
The model proposes. Your application controls access and actions.
- Task-specific assistants
- Assistants for information access, drafting, summarization, and guided workflows. We define what they can read and where a person checks the result, particularly before decisions or changes with significant consequences.
- Document processing
- Extract agreed fields, classify documents, or prepare summaries with outputs people can inspect and correct. We test representative files and agree supported formats; scanned pages, unusual layouts, and OCR needs require their own assessment.
- RAG & knowledge search
- Retrieval-augmented generation finds relevant material from authorized sources before using it to draft an answer. Source references, updates, and permissions are part of the implementation. Missing or insufficient evidence needs an explicit response; retrieval does not make every answer correct.
- Workflow automation & agents
- Bounded workflows in which a model can request defined tool actions. Application code checks permissions and validates inputs before execution. We set step limits, approval points, cancellation, and recovery behavior so a failed or repeated operation can be handled deliberately.
- AI features for SaaS & existing apps
- Add a focused capability to React, Next.js, Node.js, or PHP applications, including SaaS products and internal business tools. Existing account rules, subscription access, and interfaces guide the integration; adopting AI does not require rebuilding the product.
Engineering approach
Check the fit before building the connection.
We compare a model-based feature with simpler alternatives using agreed evaluation examples. Provider-specific code stays separate from application rules so behavior can be inspected and changed.
Understand the workflow
Identify the task, users, available data, and existing systems. Discuss what a wrong answer or failed action would mean for the people using it.
Validate the approach
Compare a model with conventional search, fixed rules, or deterministic automation. Use representative examples to decide whether AI is appropriate and what the first implementation should cover.
Implement with boundaries
Connect the selected capability with access controls, input and output validation, review steps, and failure handling. Keep provider-specific code separate from application rules.
Verify and improve
Test typical and difficult examples, inspect generated outputs and requested actions, and refine behavior within the agreed scope. Record limitations and the decisions needed for further work.
Technologies & integrations
Tools chosen for the job.
- Language model APIs
- Provider selection follows the task, data constraints, output needs, and operating budget. Credentials and provider calls stay on the server.
- React, Next.js, Node.js & PHP
- Work within the current frontend and backend, adding an interface or server endpoint where needed while keeping business rules in the application.
- Search, storage & background work
- Use existing data sources and search where suitable. Retrieval indexes, queues, or document-processing tools are added only when the workflow calls for them.
Existing systems & ongoing development
Extend the application you already use.
We assess the current integration points and dependencies, then introduce a focused feature that respects existing permissions and business rules. Supporting changes can be staged; a complete rewrite is not the starting assumption.
Before we begin
Questions about AI Integration & Automation.
Can AI be integrated into an existing app?
Often, yes. We first review the code, interfaces, permissions, and data flows, then identify where a focused feature can fit. Some systems need a new endpoint or background task; the extent of those changes is agreed after reviewing the application.
Does every automation require AI?
No. Fixed rules are often better for predictable tasks such as checking required fields or moving an approved record between systems. A model may help interpret varied text or documents, while the actions that follow can remain deterministic.
What data does an AI integration need?
That depends on the task. A document workflow needs representative files; knowledge search needs an agreed set of authorized sources and a way to keep them current. We decide what can be accessed and sent to a provider. Start with a general description rather than private documents or credentials in your enquiry.
How are incorrect outputs handled?
We define checks for output structure and business rules, a review path for uncertain results, and what the interface shows when a useful answer is unavailable. Checks cannot catch every factual error. For consequential decisions or actions, the workflow can require a person’s approval before proceeding.
Can the solution use our existing stack?
Yes, where the system provides suitable integration points. React or Next.js interfaces can use server-side model connections in Node.js or PHP. We review current dependencies and operating constraints before proposing additional tools; replacing the stack is not a requirement.
What is the difference between AI integration and AI-assisted development?
AI integration adds a capability to your application or workflow that users can use, such as document extraction or knowledge search. AI-assisted development means using tools to help with engineering tasks such as coding, debugging, or documentation. This service concerns the feature delivered in your software; development methods are a separate consideration.
Work in context
- Software Engineering
Broader founder engineering experience; these examples are not delivered runtime-AI integration cases.
Work directly with the engineer
Which task could use a carefully scoped feature?
Describe the intended workflow, your current software, and the point where people need help. We can work through the data and review requirements together. You’ll work directly with our founder, Kerem Yılmaz Güney, on the technical decisions and implementation.