Services · Remote delivery worldwide
I build AI features that connect your documents and business workflows to useful, reviewable answers. Define retrieval, validation and failure behavior before expanding a prototype into a product.
Get a free estimateDefine the expected behavior of document ingestion and retrieval against the chosen ai application development workflow. Include the inputs, visible result and recovery condition, with special attention to missing evidence. The handoff should explain the boundary another developer can safely extend.
Define the expected behavior of cited question answering against the chosen ai application development workflow. Include the inputs, visible result and recovery condition, with special attention to private source leakage. The handoff should explain the boundary another developer can safely extend.
Define the expected behavior of structured data extraction against the chosen ai application development workflow. Include the inputs, visible result and recovery condition, with special attention to invalid structured fields. The handoff should explain the boundary another developer can safely extend.
Define the expected behavior of model output validation against the chosen ai application development workflow. Include the inputs, visible result and recovery condition, with special attention to unbounded token use. The handoff should explain the boundary another developer can safely extend.
Define the expected behavior of cost and latency controls against the chosen ai application development workflow. Include the inputs, visible result and recovery condition, with special attention to provider timeouts. The handoff should explain the boundary another developer can safely extend.
Define the expected behavior of human escalation against the chosen ai application development workflow. Include the inputs, visible result and recovery condition, with special attention to missing evidence. The handoff should explain the boundary another developer can safely extend.
AI is no longer a research project — it's a product feature. I build web and mobile applications that embed large language models (LLMs) to make your product smarter: conversational interfaces, document intelligence, automated content generation, and workflow acceleration.
Every implementation is production-focused — not just a ChatGPT wrapper, but a well-engineered system with proper context management, cost control, and safety guardrails.
Planning ranges below are illustrative; a delivery schedule is agreed after reviewing your actual scope.
1–3 working days (planning assumption)
For ai application development, this step concentrates on document ingestion and retrieval and the risk of missing evidence. Identify the users, existing dependencies and the first useful workflow. Write the acceptance conditions before estimating a build. The schedule depends on access, review turnaround and the complexity of the systems involved; these ranges are planning examples, not a delivery promise.
2–5 working days (planning assumption)
For ai application development, this step concentrates on cited question answering and the risk of private source leakage. Map the interface and investigate the riskiest dependency with a narrow prototype. Capture unanswered questions instead of treating a mockup as a finished system. The schedule depends on access, review turnaround and the complexity of the systems involved; these ranges are planning examples, not a delivery promise.
Scope-dependent; estimate after discovery
For ai application development, this step concentrates on structured data extraction and the risk of invalid structured fields. Build a small vertical slice that connects the interface, application logic and persistence. Review that slice before expanding to additional workflows. The schedule depends on access, review turnaround and the complexity of the systems involved; these ranges are planning examples, not a delivery promise.
2–5 working days per agreed milestone
For ai application development, this step concentrates on model output validation and the risk of unbounded token use. Check the accepted behavior with realistic fixtures, denied actions and interrupted requests. Review accessibility, logs and recovery paths as part of the same milestone. The schedule depends on access, review turnaround and the complexity of the systems involved; these ranges are planning examples, not a delivery promise.
1–3 working days (planning assumption)
For ai application development, this step concentrates on cost and latency controls and the risk of provider timeouts. Prepare configuration notes, deployment steps and ownership. Demonstrate one normal operation and one failure recovery so maintenance does not depend on memory. The schedule depends on access, review turnaround and the complexity of the systems involved; these ranges are planning examples, not a delivery promise.
Node.js
ReactAI chat assistant backed by a knowledge graph for structured domain Q&A — grounding responses in a curated entity/relationship graph instead of free-form retrieval alone.
TODO: Add an owner-verified measurable outcome and measurement period.
AI-powered fitness and health platform delivering personalized workout and nutrition recommendations based on user goals and progress data.
TODO: Add an owner-verified measurable outcome and measurement period.
Internal tool that accepts Figma images or file paths and uses AI to auto-generate test cases and use cases. Python with Selenium for extraction. Output formatted for direct copy-paste into Word.
TODO: Add an owner-verified measurable outcome and measurement period.
TODO: Add a genuine client testimonial relevant to ai application development, with permission and a source link. No unverified quote is shown here.
Third-party subscriptions, hosting and specialist reviews are scoped separately. An estimate states assumptions and exclusions before work starts.
Starting from: TODO — owner to confirm range
A defined workflow with acceptance criteria and milestone payments. Changes outside the agreed scope receive a separate estimate.
Starting from: TODO — owner to confirm range
Useful for investigation, targeted improvements and evolving requirements. Agree on a time budget and review progress before extending it.
Starting from: TODO — owner to confirm range
Reserved capacity for maintenance and iterative delivery. Agree on included work, response expectations and a monthly review.
Start with the users and the workflow that matters most. For this service, share examples related to document ingestion and retrieval, current systems and any constraints on data access. Include a target date and budget range. This makes it possible to distinguish necessary work from optional additions before proposing a milestone.
I separate the initial workflow from secondary features, then review cited question answering and the dependencies it introduces. An estimate should list assumptions, exclusions and acceptance checks. Unknowns such as private source leakage may need a short investigation before a fixed commitment is sensible.
An existing Python implementation can be the starting point. I would review the relevant code, configuration and current behavior first, then propose a narrow change. Access to a staging environment and reproducible examples helps assess invalid structured fields without relying on live customer records.
The acceptance conditions should cover successful completion and a deliberate failure. For structured data extraction, we agree on representative fixtures and the expected user-visible state. Tests should verify the actual boundary rather than just the shape of a response. Any specialist compliance or security review is scoped separately.
The application needs a defined response to unbounded token use. Depending on the workflow, that may be a retryable state, preserved input, an operator queue or a human handoff. We should decide which actions are safe to repeat and what evidence support staff need before accepting the design.
Ownership of the ai application development deployment, credentials and operational checks is agreed before release. The handoff documents cost and latency controls and the remaining support needs. Ongoing maintenance can be discussed as a separate engagement; it should not be assumed to include unlimited features, platform changes or external-provider costs.
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