Prototype and production integrations using local LLMs or cloud AI platforms — adding features like assistants, semantic search, RAG pipelines, and content generation while keeping your data private, on-premises, and under your control.
Every engagement starts with your use case — then we pick the right architecture. These are the six most common patterns deployed for small and medium businesses.
Run Mistral, Llama, Gemma, Qwen, and other open-weight models on your own hardware or internal server using Ollama. Zero API costs, complete data privacy, sub-100ms inference.
Connect your app or website to OpenAI, Anthropic, Google Gemini, or open-weight hosted APIs through clean, versioned wrappers — with proper error handling, rate limiting, and fallback logic.
Retrieval-Augmented Generation connects LLMs to your own documents, knowledge bases, and internal databases so answers are grounded in your real data — not hallucinations.
Replace keyword search with vector-based similarity search using text embeddings and a local or cloud vector database (pgvector, Qdrant, Chroma). Find meaning, not just matching words.
Conversational assistants embedded in your website, app, or internal tool — trained on your content, brand voice, and FAQs. Works with local or cloud models depending on your privacy needs.
Automated content workflows for product descriptions, summaries, email copy, and reports — with structured prompts, human-review checkpoints, and output validation built in.
Fixed-scope engagements with clear deliverables, full source code handoff, and documentation on every project.
| Included | AI Starter | Private LLM Deploy | Production AI Build |
|---|---|---|---|
| Price | $1,500–$3,500 | $3,500–$7,500 | $8,500–$18,000 |
| Delivery | 1–3 weeks | 2–4 weeks | 4–8 weeks |
| Cloud API integration | ✓ | ✓ | ✓ |
| Local LLM (Ollama) | — | ✓ | ✓ |
| Model selection & benchmarking | — | ✓ | ✓ |
| Basic RAG pipeline | — | ✓ | ✓ |
| Vector database (pgvector / Qdrant) | — | — | ✓ |
| Document ingestion pipeline | — | — | ✓ |
| Semantic search integration | — | — | ✓ |
| Admin panel | — | — | ✓ |
| Usage analytics dashboard | — | — | ✓ |
| Post-launch support | — | — | 60 days priority |
| Source code + documentation | ✓ | ✓ | ✓ |
AI systems need maintenance — models update, prompts drift, and usage patterns change. Retainers keep your integration healthy and compounding over time.
Every AI integration follows the same four-phase process — requirements first, then architecture, then build, then handoff.
Need one specific piece rather than a full package? These standalone services are available individually or as add-ons to any existing project.
AI isn't just for tech companies. These are the most common integrations built for small and medium businesses in 2025–2026.
Answer FAQs, pull from your knowledge base, and hand off to a human when needed — available 24/7, no extra staff.
Search across PDFs, wikis, and internal docs using natural language — employees find answers in seconds, not hours.
Feed in specs, get polished, on-brand product descriptions at scale — consistent tone, no copywriter bottleneck.
Classify incoming requests, extract key fields, and route them to the right team or workflow — before a human even reads it.
Upload long reports, meeting transcripts, or data exports and get concise, structured summaries your team can act on immediately.
Translate and localize marketing copy, product pages, or support content at a fraction of the cost of professional translation agencies.
Enterprise AI shops charge for team overhead and vendor relationships you don't need. You get direct access to the person building the system — and own everything when it's done.
| Agency / Platform Drawbacks | conxion visual communications Advantage |
|---|---|
| ❌ $10,000–$50,000+ for local LLM setups at enterprise firms | ✅ Private LLM Deploy from $3,500 — same capability, SMB-priced |
| ❌ Ongoing per-token API costs that compound as usage grows | ✅ Local deployments = zero ongoing API costs — your hardware, your model |
| ❌ Your data leaves your network every time you call a cloud API | ✅ Local LLM keeps all data on-premises — nothing sent to third-party servers |
| ❌ "Black box" integrations — no code access, no portability | ✅ Full source code handoff — you own everything, no vendor lock-in |
| ❌ Generic implementations not tailored to your actual use case | ✅ Built around your data, your stack, and your workflow |
| ❌ Account managers relay feedback through layers | ✅ Direct access — you talk to the builder, not a project coordinator |
Local LLM deployments keep every query, document, and response inside your network. Nothing leaves your premises — ever.
Every integration is delivered as clean, documented source code. You can extend it, hand it to another developer, or run it forever — no subscriptions, no lock-in.
The best model for your use case isn't always the biggest or most expensive. We benchmark 3–5 candidates against your actual tasks before recommending one.
Need an AI assistant embedded in a new website? The UI design, front-end build, and AI integration all come from the same person — no coordination overhead.
Need a quick integration audit, a model recommendation, or a one-off consultation before committing to a package? I offer flexible hourly engagements — no minimum, no retainer required.
Common questions about AI integrations, local LLMs, and how these projects work.
A cloud API (like OpenAI) sends your prompt data to a third-party server, incurs a per-token cost, and requires an internet connection. A local LLM runs the model on hardware you own — nothing leaves your network, there are no per-token fees once it's deployed, and latency is often lower. The trade-off is that local models typically require capable hardware and occasional maintenance, whereas cloud APIs scale automatically. For privacy-sensitive workloads or high query volumes, local deployment usually wins. For quick integration or variable usage patterns, cloud APIs can be the better fit.
It depends on the model size and query volume. For most SMB use cases — internal assistants, document search, classification — a workstation or server with a mid-range NVIDIA GPU (8–24 GB VRAM) running a 7B–13B parameter model is sufficient and costs $800–$2,500 new. If you already have suitable hardware, you may need zero additional investment. As part of any local LLM engagement, we'll benchmark your current hardware before recommending whether to use it, upgrade it, or select a smaller model that fits what you already have.
For local deployments: Llama 3, Mistral, Mixtral, Gemma, Qwen, Phi, and others served through Ollama. For cloud APIs: OpenAI (GPT-4o, o4-mini), Anthropic (Claude), Google Gemini, and open-weight hosted options like Together AI or Fireworks AI. Model selection is always use-case-driven — we benchmark 3–5 candidates against your actual tasks rather than defaulting to whatever is currently trending.
Yes — most integrations are added to existing systems rather than built from scratch. A chat assistant, semantic search widget, or content generation endpoint can typically be added to any website or app regardless of its stack or age, as long as it has the ability to make HTTP requests or embed a script. The discovery call at the start of every project maps your current stack to the integration approach that fits it best.
The safest option is a local LLM deployment where no data ever leaves your network. If cloud APIs are used, sensitive fields can be redacted or anonymized before the prompt is sent, and results re-enriched on the way back. API provider data-handling policies (OpenAI's Enterprise tier, Anthropic's API) can also be configured for zero data retention. Every engagement includes a data flow diagram so you can see exactly where your information goes at each step.
Yes — all AI integration work is done remotely by nature. Requirements gathering, reviews, and handoff all happen via video call, email, and shared documents. For local LLM setups that involve physical hardware, remote installation via SSH or VPN is the standard approach; an in-person visit can be arranged for clients in the Piedmont Triad area.
Let's start with a free consultation — I'll review your use case and recommend whether a local LLM, cloud API, or hybrid approach makes the most sense for your budget and privacy requirements.