What does it cost to build something like this?+−
We work in short cycles (weeks, not quarters), so costs are scoped per engagement rather than a large upfront contract. A typical project starts with a Week 1 scoping phase, followed by a 2–4 week working prototype, then hardening for production in weeks 5–8. Ongoing support retainers are optional. Pricing varies by complexity, but our model is built around being cost-conscious — multi-model routing alone typically cuts AI inference costs by 40–70%.
How do you stop the AI from making things up or doing damage?+−
Two mechanisms. Sandboxed agents: every agent runs inside an isolated E2B sandbox, with every action contained, reversible, and logged, so the blast radius of any mistake stays small. Hardening phase: weeks 5–8 of every build are dedicated to evals, observability, cost guardrails, and safety engineering — the unglamorous work that determines whether AI survives past month two.
Will our data leave our environment or go to train someone's model?+−
We build explicitly on infrastructure you own, using open protocols like MCP — that's the no-lock-in point: if you stop working with us, your systems keep running. We use production-grade providers (Anthropic, OpenAI, open-weights) where data handling is governed by enterprise API agreements, not training pipelines. Confirm specifics per model provider during your discovery call.
Do we need an in-house ML team to maintain what you build?+−
No. Our handover phase includes documentation your team can actually read, plus an optional ongoing support retainer. Our philosophy: "If you want us out by month three, that’s fine — we plan for it from day one." We build with open protocols so you’re never dependent on us or any single vendor.
How is multi-model routing different from just using GPT-4 or Claude?+−
Using a single model means paying frontier prices for every task, even simple ones. We orchestrate across multiple providers — Anthropic, OpenAI, open-weights like Llama, Mistral — routing simple tasks to cheap, fast models and complex tasks to frontier models. The result is typically a 40–70% cost reduction without quality loss, plus higher uptime since there's an automatic fallback if any one provider goes down.
What is MCP and why should we care?+−
MCP (Model Context Protocol) is becoming the standard way AI assistants talk to internal systems — databases, CRMs, ticketing tools, proprietary APIs. We build custom MCP servers that expose your internal tools to any compliant AI (Claude, ChatGPT, etc.) securely and with the right permissions. Why it matters: your AI integrations won't be locked to one vendor or one model — whatever model is best next year, it'll already be able to talk to your systems.
How quickly can we see something running?+−
Pretty fast. Our engagement rhythm is Week 1 for scoping and problem framing, then weeks 2–4 for a first working prototype in your stack, with real data. Expect something live and testable within 3–4 weeks of kickoff.
What if the model gets better in six months and we want to switch?+−
This is exactly what our architecture is designed for. Because we build on open protocols (MCP) and use multi-model routing infrastructure, swapping out an underlying model is a routing change, not a rebuild. No lock-in by design means your systems keep running regardless of which model you use next.
Can you also help us with the data foundations underneath?+−
Yes — this is actually a core differentiator. We've been doing data engineering and analytics since 2017, well before the AI wave. Our principle is “data foundations come first” — no AI works without clean data underneath. We can build the warehouse, the pipelines, and the AI model within the same engagement, so you're not stitching together separate vendors.
