Use-case & data assessment
We define what the system should answer or do, review your documents and data sources and identify access and privacy requirements.
- Use-case definition
- Data source inventory
- Access and privacy requirements
Policies, manuals, proposals, reports and past contracts are spread across shared drives, email and old systems. Staff ask colleagues the same questions, new hires take months to find their way and good work gets redone because nobody can find the earlier version.
General chatbots don't know your documents and shouldn't be trusted with them casually. A custom LLM integration with retrieval-augmented generation (RAG) answers from your own content, shows its sources and respects who is allowed to see what.
We define what the system should answer or do, review your documents and data sources and identify access and privacy requirements.
We design the retrieval pipeline and choose models from OpenAI, Anthropic, Google or open-source options based on quality, cost and data handling.
We build document ingestion, search, answer generation with citations and the interface, whether a standalone tool or features inside your app.
We test answer quality against a question set, monitor usage and costs, and keep the index current as documents change.
The detail
LLM integration means connecting a large language model, the technology behind tools like ChatGPT, Claude and Gemini, to your own systems and data so it can do useful work inside your organization. At Venda Technologies we build custom LLM integrations for Kenyan businesses, NGOs, professional firms and software teams: internal knowledge assistants that answer from your documents, and AI features built directly into your web applications.
Most of these projects rely on retrieval-augmented generation, usually called RAG. Instead of expecting the model to know your policies, RAG searches your documents for the most relevant passages and gives them to the model along with the question. The model writes an answer based on that material and cites where it came from, so users can check it.
A private assistant that staff use to ask questions like "What is our leave policy for contract staff?" or "Which past proposals covered water projects in Kisumu?" and get an answer with links to the source documents. Common sources include HR and finance policies, operating procedures, project reports, product manuals, contracts and past proposals.
For software products and customer portals, we add AI capabilities such as natural-language search, summarization of records, draft generation, classification and structured data extraction. These are built into your existing application, often alongside our web application and portal development work, and use the APIs from OpenAI, Anthropic and Google.
Where a public assistant needs to search a large library, such as product catalogues or technical documentation, we use the same RAG approach. For simpler FAQ-style needs on your website or WhatsApp, our AI chatbot development service is usually a better fit.
OpenAI, Anthropic and Google each offer capable models through business APIs, with different strengths, pricing and data handling terms. Open-source models can also be hosted on infrastructure you control where data requirements are strict, though they need more engineering and hosting effort. We compare options against your use case and design a model-agnostic layer so you can switch providers without rebuilding the application.
Personal data in your documents matters. The Kenya Data Protection Act, 2019 places obligations on organizations to protect personal data, limit its use to legitimate purposes and consider where it is transferred. We help by excluding sensitive documents that don't need to be included, applying access controls, using provider settings that limit data retention and documenting data flows so your data protection officer or legal adviser can review them.
A RAG system is only useful if people trust it. We track:
Not sure where to start? Our AI strategy and readiness service helps prioritize use cases, and you can see the full range of our AI integration and automation services.
Tell us which documents your team searches most and the questions they keep asking. We will outline how an LLM integration could help and what it would take. Book a discovery call to begin.
FAQ
Can't find your answer? Ask our team — we reply within one business day.
Retrieval-augmented generation (RAG) searches your own documents for relevant passages and gives them to an AI model when answering a question. It lets the model answer from your up-to-date information, cite sources and avoid relying on general knowledge that may be wrong or outdated for your organization.
It depends on the number and condition of your data sources, access control needs, whether it is a standalone tool or built into your app, and expected usage, which drives AI API and hosting costs. We quote after a discovery call and separate build costs from monthly running costs.
Business API offerings from major providers generally state that customer data is not used for model training by default, but terms differ and change, so we review the current terms with you. We also limit what data is sent, configure retention settings where available and consider self-hosted models for stricter needs.
Yes, and it should. We record permissions for each document and filter search results by the user's role before anything reaches the AI model, so staff only get answers drawn from documents they are already allowed to access, and every query is logged.
Each has strengths, and the best choice depends on your task, language needs, cost and data terms. We test candidates against your own questions rather than relying on general benchmarks, and design the system so you can switch providers later.
It depends on how many sources you have, how organized the documents are and the permission rules involved. A pilot over one well-organized document set is much quicker than a company-wide rollout. We give a timeline after reviewing your data in discovery.
We collaborate as your digital teammates, not just outsourced vendors. No tech jargon to confuse you—just transparent milestones, flawless execution, and a platform built to scale.
We don't guess. We get to the absolute root of your business objectives, target audience, and brand energy before we write a single line of code.
We craft high-end wireframes and intuitive interfaces. You get to see the exact blueprint and visual identity of your project, ensuring it feels flawlessly "you."
This is where the stylish nerds take over. We build your platform using clean, mobile-first custom code, rigorous QA testing, and bank-grade security protocols.
Go live with absolute confidence. We deploy your digital engine to the world, providing a seamless hand-off, backend training, and ongoing performance monitoring.
We don’t build generic templates; we engineer digital assets that scale. Explore our latest projects across custom web architecture, e-commerce, and high-end brand strategy.
Why Venda for custom llm integration & rag
We build production web applications, so our AI features come with the parts that demos skip: authentication, permissions, logging, error handling and cost control. We work across OpenAI, Anthropic and Google APIs and design so you can switch models later. We measure answer quality with real questions from your team, and we are clear with you about what the system can and cannot reliably do.
Have a look at what our clients have said about our web design agency in Kenya
Everything you need to know before we start working together.
Never. We build custom front-end interfaces and CMS architectures from scratch. Most agencies buy a $50 theme, slap your logo on it, and call it a day—resulting in slow, bloated, and easily hacked websites. We engineer scalable, secure platforms tailored exactly to your brand and operational needs.
We don't offer generic "packages" because we don't build generic websites. Costs depend entirely on the complexity of the build—a custom 5-page corporate site requires a different architecture than a multi-vendor e-commerce platform. After our initial discovery call, we provide a transparent, itemized proposal so you know exactly what you are paying for.
For a custom corporate website or brand identity project, our standard turnaround is 4 to 6 weeks. For complex custom portals, Learning Management Systems (LMS), or heavy e-commerce builds with custom API integrations, expect an 8 to 12-week timeline. We map out strict delivery milestones before we begin.
Yes. We don't use clunky manual "Paybill instructions" pages. We build seamless Lipa na M-Pesa STK push integrations (along with Pesapal, Stripe, and PayPal) so your customers can complete transactions instantly without ever leaving your checkout page.
You do. 100%. Once the final invoice is settled, we hand over full administrator access, source files, and asset libraries. We don't hold our clients' digital properties hostage to force them into paying ongoing fees.
Absolutely. While you have the power to update your own content, maintaining the server, updating security patches, and monitoring uptime requires technical expertise. We offer dedicated, monthly maintenance retainers so your site remains lightning-fast and bulletproof long after launch.
No honest agency can guarantee a #1 spot due to Google's constantly changing algorithms. What we do guarantee is best-in-class technical SEO execution, clean code, lightning-fast load times, and data-driven keyword strategies that consistently push our clients to the top of their industries over a 3 to 6-month period.
