Scope of deliverables
Assess and build AI features for your web apps and internal systems, from API architecture to private knowledge base retrieval.
Verified project deliverables
- Use case & technical feasibility assessment
- Approved knowledge source preparation & RAG indexing
- Custom web assistant or internal tool UI
- Permission boundaries & role-based access controls
- Latency, token cost & output evaluation
- Production deployment & API documentation
Approved knowledge retrieval (RAG) for business teams
Generic chatbots often guess when answering company-specific questions. We build Retrieval-Augmented Generation (RAG) systems that restrict answers strictly to your verified internal manuals, policy documents, product catalogs, and standard operating procedures (SOPs).
Key specifications & considerations
- Private vector indexing over approved internal documents only
- Exact source citations and document links provided with every response
- Transparent fallback to human staff when verified information is absent
- Scheduled automated re-indexing when source documentation is updated
Custom AI capabilities embedded into web interfaces
Enhance your customer-facing website or internal web application with purpose-built AI features that solve specific user friction points:
Key specifications & considerations
- Intelligent website assistants answering inquiries from published service pages
- Acoustic speech and pronunciation assessment interfaces for learning platforms
- Real-time multilingual translation and localized content adaptation
- Smart form auto-completion and document validation assistants
Role-based access controls and token cost governance
Enterprise security requires that AI features respect existing user permissions. A junior staff member or website visitor should never be able to query executive payroll or confidential client records through an AI prompt.
Key specifications & considerations
- Role-based document filtering ensuring users only access authorized data
- Prompt injection defense and adversarial input filtering
- Token rate-limiting and monthly budget caps to keep operating expenses predictable
- Sub-second response caching for frequently asked organizational queries
Production testing, monitoring, and handover
We benchmark integrations against a comprehensive evaluation suite of normal, edge, and out-of-scope test cases before deployment. We monitor latency, error rates, and user feedback in production.
Key specifications & considerations
- Automated regression test suite of benchmark questions and expected outputs
- Real-time latency and token usage monitoring dashboards
- Complete source code, deployment scripts, and architecture handover
What to prepare before we begin
Share your current website or software stack, the desired AI feature, and the documentation or systems it needs to interact with. Prepare a small sample of approved source materials with confidential customer records removed. Email simkieu@gmail.com to discuss feasibility.
Direct contact for project inquiries: +1 669 777 6562 simkieu@gmail.com
Frequently asked questions
What is the difference between an AI workflow and an AI integration?
An AI workflow connects multiple existing tools via background triggers and automations. An AI integration embeds AI functionality directly into your custom software, web application, or customer-facing portal.
How do you ensure the AI does not answer out-of-scope questions?
We constrain the system prompt with strict negative constraints, verify facts against your private vector database, and instruct the model to politely decline or escalate any query not supported by approved reference material.
Can this be hosted on our own cloud infrastructure?
Yes. We can deploy the solution directly to your own Cloudflare, AWS, Google Cloud, or Microsoft Azure environment, ensuring you maintain full administrative sovereignty and source code control.
What ongoing software fees should we anticipate?
You pay model API token consumption fees directly to providers (e.g. Anthropic, OpenAI) without agency markups. For typical small to mid-sized business usage, API costs are generally modest (often under $30 to $100 per month).
Planning resources
AI integration
AI integration checklist: data, permissions and human review
Prepare an AI integration with a practical checklist for approved data, API access, human review, failure handling, test cases and ongoing software costs.
Read the guide