Coordinated agents. Across your tools. Without the demo-ware.
Multi-step workflows where one agent reads, another writes, a third escalates. Claude with function calling, pgvector for memory, Inngest as the runtime.
Not a chatbot wrapper. A pipeline of model calls, tool use, retrieval, and approvals — with a review queue and an audit log.
Multi-LLM platform with Claude + GPT routing, role-based access, audit log, per-team budgets. Built for a team that needed AI with governance.
What ships, by name
We name the parts because the parts are how you decide whether we know what we are doing.
Provider-agnostic from day 1
Claude Sonnet 4.6 default with prompt caching. GPT-5/4o switch-in where function-calling or vision wins. Gemini Flash for cost-bounded high-volume work. Vendor failover wired at the SDK layer, not the prompt layer — you're never locked to one vendor's price curve.
MCP-compatible tool servers
We implement Model Context Protocol-compatible tool servers when the build benefits from the open standard — Cursor, Claude Desktop, IDE integrations. For closed internal tools, direct function-calling is faster. We pick per build, not per slogan.
Multi-step orchestration
Agents that read your queue, call the right tool, hand off to the next agent, and write back to Postgres. Inngest or Trigger.dev as the runtime — not a hand-rolled `setTimeout` loop.
Tool use, structured outputs
Claude with function calling for structured output. JSON schema on every tool boundary. Outputs validated with Zod before they touch your database.
Memory + retrieval
Pgvector on Postgres for embeddings. Per-tenant scoping. Hybrid search (BM25 + vector) where one mode is not enough. Cached embeddings to keep cost flat.
Human-in-the-loop, by default
Low-confidence outputs route to a review queue. Approval action flips state and triggers downstream. Audit log captures every model call and every human override.
Observability, not vibes
Sentry on errors, traces on every agent run, token-spend per task in your admin panel. You see what an agent did, what it cost, and where it fell over.
Cost ceiling, in code
Prompt caching on Claude. Per-tenant token budgets. Cheap model first, escalate only when needed. Hard limits enforced server-side, not in a prompt.
How a build runs
Spec, ship, measure. No discovery phases.
Map the workflow
Where does the agent read, write, escalate? Which boundaries need human review? Spec written and signed in 48 hours.
Pick the model
Claude Sonnet vs. Opus vs. GPT-5/4o. We test on your real data and your latency budget before locking the architecture.
Build the pipeline
Inngest jobs, function calling, pgvector retrieval, review queue, admin panel. Vercel preview from week one.
Ship with guardrails
Sentry on errors, token budgets enforced server-side, audit log on every call. You see cost and quality in real time.
Pricing
Build / Scale / Consult. Same tiers across every Wolrix service.
Single agent pipeline. One workflow, one model, review queue, admin panel. Production-ready.
Multi-agent ecosystem. Several pipelines, retrieval, per-tenant budgets, full observability. Built for governance and scale.
Architecture review for an existing agent system. Cost audit, retrieval audit, eval design.
Satisfaction guarantee
100% satisfaction or your money back
Fixed scope, fixed price, fixed timeline. If you're not satisfied with the deliverable, full money back. Same policy that hit 100% Job Success across 42 Upwork projects.
Every feature documented before work begins.
The quote is the quote. No surprise invoices.
Source, repo, deploy access. Full handover.
Send the workflow.
Tell us what the agent should do, what tools it should call, what humans should approve. Fixed quote in 48 hours. NDA before any details.