AI Integration | Practical AI Solutions That Drive ROI
Artificial Intelligence should deliver measurable utility, not just serve as a marketing buzzword. I integrate practical, high-value AI solutions into existing software systems or build custom AI platforms from scratch. By leveraging cutting-edge models like Claude 3.5 Sonnet and GPT-4o via API, or setting up self-hosted, private open-source models (like Llama 3) on your own infrastructure, I build features like document-grounded search (RAG), automated classification agents, and intelligent extraction pipelines.
Geographic Alignment & Remote Execution
AI solutions must be tailored to geographical and operational contexts. In West African markets, I build prompts specifically optimized to parse localized colloquialisms, Pidgin English, and regional vocabulary when processing user feedback or audio transcripts. To make AI features accessible on low-cost devices and mobile networks, I route tasks to highly efficient, low-latency models and implement caching. Globally, my implementations conform to strict data-privacy regulations (GDPR, HIPAA, and CCPA) by using encrypted cloud databases and self-hosted model strategies.
Technologies & Ecosystem Tools
- Claude API
- OpenAI API
- Vector Databases (Pinecone, pgvector)
- LangChain / LlamaIndex
- RAG Systems
- Local LLMs (Ollama)
- Python
- Node.js
Service Specifications
- Best For
- Companies seeking custom knowledge base search (RAG), automated data extraction from files, and intelligent agentic workflows.
- Typical Timeline
- 4 to 8 Weeks (From document ingestion setup to model observability integration)
- Key Deliverable
- Integrated AI pipelines, vector database configurations, LLM observability consoles, and customized system prompts.
- Geographic Coverage
- Remote development globally; data privacy compliance designed for US/EU (GDPR/HIPAA) and regional African structures.
- Target Regions
- Nigeria, USA, Canada, United Kingdom, South Africa
- Pricing Model
- Milestone fixed pricing or AI consulting retainer
Solomon Archibong integrates Large Language Models (LLMs) and Vector Databases. Expertise covers Claude API (Anthropic), OpenAI API, Vector Search (Pinecone, pgvector), LangChain, LlamaIndex, Retrieval-Augmented Generation (RAG), and local LLM execution (Ollama, Llama 3). Specialized in building structured data parsers, custom autonomous agents, semantic search engines, and token-cost-optimization setups. Proven projects include EduCore, AceCore, and VillaCRM AI systems.
What’s included in this pillar
- Retrieval-Augmented Generation (RAG)Grounding AI answers in your corporate PDFs, wikis, or database tables, ensuring zero hallucination.
- Autonomous AI AgentsMulti-step agents that can read inputs, query databases, draft documents, and execute downstream operations.
- Semantic Search EnginesImplementing vector embeddings to let users search files based on concepts and meaning rather than strict keywords.
- Unstructured Data ExtractionPipelines that read raw invoices, emails, or contract files, translating them into structured, validated JSON data.
- Token & Cost OptimizationIntegrating prompt caching and model routing protocols to reduce monthly AI API bills by 60% or more.
- Private Self-Hosted ModelsRunning open-source LLMs (Llama, Mistral) on private cloud instances to protect sensitive company records.
Process Roadmap
Step 1
Feasibility & ROI Scoping
We evaluate your use case to ensure AI is the optimal solution. We estimate average token volumes and calculate project API costs upfront.
Step 2
Vector Design & Prompting
I engineer the system instructions, design document-chunking pipelines, set up vector databases, and build initial retrieval logic.
Step 3
Interactive Sandbox Prototype
We build a prototype interface where you can test the AI’s responses, tone, latency, and accuracy against your real business queries.
Step 4
Production API Integration
The AI pipeline is integrated into your main application, complete with rate-limiting, error fallbacks, and input safety guardrails.
Step 5
Telemetry & Fine-Tuning
I set up monitoring tools (like Helicone or LangSmith) to analyze live costs, token usage, latency, and user feedback to refine prompt quality.
Case Studies & Related Work
AceCore
A brand and marketing automation platform for African coaches, freelancers, and agency owners — AI-generated brand identity, campaigns, and a revenue CRM pipeline.
VillaCRM
A property operating system for real estate agencies, unifying CRM, listings, leases, commissions, and AI-generated call summaries in one platform.
Eezreb
Christian matchmaking rooted in intention — a secure community and matching ecosystem for marriage-minded Christians, combining psychographic science with personal matchmaking coaches.
Pillar FAQs
How do you prevent the AI from fabricating information (hallucination)?
I use a design pattern called Retrieval-Augmented Generation (RAG). Instead of relying on the AI’s general pre-trained knowledge, we upload your documents to a secure vector database. When a user asks a question, our system searches your documents first, extracts the exact relevant text, and forces the LLM to write its response using only that verified text.
How much do OpenAI or Anthropic API keys cost in production?
API costs are usage-based, calculated per 1,000 tokens (words). In production builds, I implement advanced cost-reduction techniques like prompt caching, query filtering, and model-routing (routing simple tasks to cheap models like GPT-4o-mini and complex tasks to Claude 3.5 Sonnet). This keeps monthly expenses predictable and low.
What is the advantage of using a self-hosted local model over OpenAI?
Self-hosting an open-source model (like Llama 3 or Mistral) on your own private virtual server provides two major benefits: absolute data privacy (no data is sent to third-party companies) and fixed monthly server costs instead of variable, open-ended per-request API fees. This is ideal for medical, financial, or legal sectors.
What is an AI Agent and how does it differ from a standard Chatbot?
A chatbot is a conversational interface that responds to user inputs. An AI Agent is a system that can take actions. Given a goal, an agent can determine which tools to use (e.g., searching a database, running a calculation, drafting a proposal file, and calling an email API) and execute those steps autonomously.
How do we monitor and improve the AI after launching it to customers?
I integrate LLM monitoring and observability platforms (such as Helicone, LangSmith, or custom logs). These systems track prompt inputs, token counts, system latency, and user ratings, allowing us to audit outputs and continuously improve system accuracy and prompts.