ByteTan

AI that ships, not just demos.

ByteTan builds LLM applications, autonomous agents, and intelligent automation — grounded in your data, bounded by your policies, and ready for production.

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The problem we solve

Most organizations have experimented with AI. Few have moved it into daily operations. The gap is not the model — it's architecture: data grounding, tool boundaries, monitoring, human handoff, and compliance with how your business actually works.

We close that gap.

What we build

LLM Applications & RAG

Applications grounded in your data.

Custom applications powered by large language models, connected to your documents, databases, and knowledge bases. We build retrieval-augmented systems that answer questions, summarize content, and reason over your data — with explicit boundaries on what they can and cannot claim.

When you need it

  • Customer-facing chat that must be accurate, not just fluent
  • Internal search that understands intent, not just keywords
  • Document Q&A across contracts, policies, manuals, or knowledge bases
  • Onboarding and training copilots for new team members

What we use: Anthropic Claude, OpenAI GPT, open-weights via Ollama or vLLM. Vector stores: pgvector, Qdrant, or Pinecone. Orchestration: Vercel AI SDK; LangChain when justified, direct SDK when not.

Typical engagement: 2–6 weeks initial build · optional monthly improvement retainer · self-hosted or cloud · your data, your model, your choice.

AI Agents

Software that takes action — within boundaries you control.

Autonomous agents that draft, schedule, triage, route, and escalate — not open-ended chatbots. Single agents for focused tasks; multi-agent setups for complex workflows. Every tool call is logged. Sensitive actions require human approval by default.

When you need it

  • Sales: qualify inbound leads, schedule calls, hand off to humans at the right moment
  • Support: triage tickets, draft replies, escalate complex cases
  • Operations: monitor systems, route alerts, execute playbooks
  • Marketing: research topics, draft content, prepare scheduling queues

What we use: Tool-calling LLMs, structured prompts, hard guardrails on tool execution, full audit logs, human-in-the-loop gates.

Tan, the assistant on this site, is built on this stack. Ask it about ByteTan's services.

Typical engagement: 3–8 weeks per agent · pilot first, scale second · approval workflows by default.

AI Workflow Automation

Intelligence between the systems you already use.

We connect your existing tools with AI in the middle — not generic Zapier-with-a-chatbot, but purpose-built workflows that understand context, handle exceptions, and stay observable when inputs are messy.

When you need it

  • Manual data entry that follows rules someone has to interpret
  • Repetitive review work (invoices, contracts, applications) with human verification
  • Multi-step processes across CRM, email, spreadsheets, and internal tools
  • Anything you do the same way every time — except when you don't

What we use: Custom integrations over off-the-shelf glue. n8n, Temporal, or direct SDKs depending on reliability needs. Always observable, always reversible.

Typical engagement: 1–4 weeks per workflow · scoped by outcome, not connector count.

ML & Predictive Analytics

Models that predict — and keep working after launch.

Classification, scoring, forecasting, and anomaly detection built on your historical data. Deployed where you need them, monitored after launch. We don't train a model and disappear — unattended models degrade.

When you need it

  • Customer churn or lifetime-value prediction
  • Demand forecasting and inventory planning
  • Lead scoring and revenue attribution
  • Anomaly detection in operations, fraud, or quality
  • Computer vision for inspection, OCR, or counting

What we use: scikit-learn and XGBoost for tabular; PyTorch for deep learning; MLflow for tracking. Cloud or your own GPU — whatever the problem needs.

Typical engagement: 4–12 weeks for first model · monitoring and retraining cadence included · explanations included.

How we deliver AI safely

Grounded outputs

RAG and tool access over verified data — not free-form guessing

Bounded tools

Agents can only call functions you approve, with rate limits and quotas

Human handoff

Sensitive decisions escalate to a person, not an algorithm

Audit logs

Every agent action is recorded and reviewable

Moderation

Input and output safety checks on public-facing agents

Spend controls

Token budgets and circuit breakers prevent runaway costs

See it in action.

Tan is ByteTan's AI assistant — built with the same agent architecture we deliver for clients. Ask about our services, describe your project, or find out if we're a fit.

Engagement model

  1. 1Discovery call — scope, constraints, data availability
  2. 2Technical assessment — architecture, risks, pilot definition
  3. 3Pilot build — working prototype with real data
  4. 4Production hardening — security, monitoring, documentation
  5. 5Iterate — monthly improvement or support retainer

FAQ

When justified by data volume and use case. We often achieve strong results with RAG and prompt engineering first — lower cost, faster iteration.

Ready to move AI into production?

Tell us about your use case and we will outline a sensible pilot.

Discuss Your Project