We embed production-grade artificial intelligence into the products people actually use — not demo videos.
Praxvon ships production-grade AI for SaaS, e-commerce and service brands — LLM agents, RAG pipelines, OpenAI / Claude / Gemini integrations, with the evaluation harnesses, observability and cost-control most teams skip. We design systems that survive contact with real users, not demos.
Every team is buying AI. Few are shipping it. The gap between a working prototype and a system your users trust in production is where most projects die — hallucinations, latency cliffs, prompt drift, runaway token bills.
Praxvon designs AI integrations that survive contact with real users. From RAG pipelines grounded in your own data to multi-step agents that act on your tools, we build the parts the demos hide: evaluation harnesses, observability, fallback paths, and cost control.
Multi-step agents that plan, call tools, and recover from failure — built on Claude, GPT-4, or open-weight models depending on your privacy and cost constraints.
RAG pipelines that ground responses in your documents, databases, and knowledge bases. We tune embeddings, reranking, and chunking for your specific corpus.
AI-powered automations that replace manual work in support, sales, and operations — wired into Slack, email, CRMs, and internal tools.
Production AI without measurement is a guess. We set up eval suites, prompt versioning, and tracing so you can ship changes with confidence.
We map your workflows, identify the AI-shaped problems (and dismiss the AI-shaped illusions), and prototype the highest-leverage one first.
Iterative builds with eval-driven development. Every prompt change is tested against a regression suite before it ships.
Production deploy with cost dashboards, latency monitoring, and drift detection. We hand off something you can operate, not a black box.
A model-and-prompt prototype is the easy 20% of an AI feature. The other 80% is what nobody films a launch video about: an evaluation suite that catches regressions before users do, prompt versioning so you can roll back a bad iteration, fallback paths when the primary model is down or rate-limited, observability that lets you see why a specific answer was wrong, and cost dashboards that surprise you in the right direction.
Moditra runs a three-language chat translator (TR / EN / AR) on Gemini Flash with a lazy cache, audit-logged per-message cost and graceful degradation when the API blips — see the Moditra case study for the rest of the system. None of that infrastructure shows up in a demo. All of it shows up the first time a real customer sends a message at 03:00.
There's no universal answer — the right model depends on the data you can send out, the latency budget, the per-message cost, and how often you'll need to swap the model under it. Sensitive data and tight cost budgets push toward open-weight self-hosted (Llama, Qwen). Long-context reasoning and code generation favor Claude. Mass-market multimodal volume often favors Gemini Flash. GPT-4 and GPT-5 still win on edge cases nobody else handles well.
We architect for swappability from day one. The model is configuration, not a hard-coded assumption. The cost of being wrong about today's model choice is a config change next quarter, not a rewrite.
Retrieval-augmented generation is the default recommendation in every AI consultancy pitch. It's also the wrong answer for a surprising number of problems. If your corpus fits in a single prompt, prompt engineering beats RAG. If your task is classification or extraction, a fine-tuned smaller model often beats a RAG-augmented frontier one — cheaper, faster, more deterministic. RAG is right when you have a large, frequently-updated knowledge base that the model can't memorize.
We start by diagnosing which class of problem you're actually solving. The first conversation is whether AI is even the right tool. Sometimes the answer is search, an explicit workflow, or a smaller deterministic model — and saying so up front is cheaper for everyone than discovering it three months in.
It depends on what you're optimizing for. Claude wins on long-context reasoning and structured output. GPT-4 / GPT-5 handle edge cases other models trip on. Gemini Flash is cost-effective for high-volume multimodal traffic. We benchmark against your actual prompts before recommending one — and we build the integration so the model is swappable.
Yes. Open-weight models (Llama, Qwen, Mistral) can run on your own GPU servers or via private inference providers like AWS Bedrock and Azure OpenAI Service. We've helped teams choose between hosted and self-hosted based on real cost projections, not vendor claims.
Token cost is rarely the surprise — context bloat and retry storms are. We instrument the pipeline before you scale: per-request token counters, cache-hit rates, automatic backoff on retries, and budget alerts. You'll know whether the next 10x of usage costs $300 or $3,000 before you flip the switch.
Mostly from scratch with focused libraries (Vercel AI SDK, the model providers' own SDKs, a thin task router). LangChain and similar abstractions hide more than they help once you need to debug a production agent. The line of code count is similar; the surprise count is far lower.
Eval suites — a curated set of inputs with expected (or graded) outputs that we run on every prompt change. Combination of automated grading (LLM-as-judge for subjective tasks, exact-match for structured ones) and spot-checked human review for the edge cases. The eval suite ships with the product, so the team owns the regression line, not us.
Yes — and that's usually where the value is. Most production AI projects are integrations, not standalone products. We've connected LLMs to HubSpot, Slack, Postgres, S3, internal Fastify and Django APIs — wherever the action actually has to happen.
Tell us your problem. We'll tell you whether AI is the answer.