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Vendor-independent by design
Provider routing and fallbacks so your product survives model deprecations, price hikes, and silent regressions.
[ 01 ] // LLM-NATIVE INFRA · EST. 2024
We're not an AI studio. We're the technical team you should have hired first.
LLM-native infrastructure for teams that need it to actually work. Multi-model architecture, productive agents, RAG, platform & infra.
[ 02 ]// Why MeG4
The unglamorous layer that decides whether your AI runs at 3am.
02 / 04
Production, not demos
Guardrails, retries, observability and traces — the unglamorous layer that decides whether your AI feature actually runs at 3am.
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Evals as a first-class citizen
Continuous evaluation pipelines so quality is measured, not assumed. We ship when the numbers say so.
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Cost-aware architecture
Token budgets, caching, and routing decisions tuned to your unit economics — not the model vendor's roadmap.
[ 03 ]// Services
What we build. Sprints, retainers, embedded teams.
Four capability tracks. Picked together or alone — always wired with evals, traces, and a fallback plan.
S01 ── /multi-modelrouting · fallbacks · evals · cost-aware
Multi-model architecture
Provider routing, fallbacks, continuous evals. Your product stops depending on a single vendor — and stops breaking when that vendor changes the model.
Sprint or retainer
S02 ── /agentstools · retries · guardrails · traces
Productive agents
Orchestration with tools, retries, observability. They run real tasks — not guided demos. With guardrails that matter in production.
From 4-week sprint
S03 ── /raghybrid · freshness · long-tail
RAG & ingestion
Hybrid search, freshness, evals. Built for the long tail — not just the demo's happy path.
Scoped per corpus
S04 ── /infraqueues · workers · otel · deploys
Platform & infra
Queues, workers, traces, deployments. The boring layer that decides whether your AI feature actually scales beyond the launch tweet.
Embedded team
[ 04 ]// Process
How we work. Four steps, no theater.
A working method built for teams shipping production AI — not for slide decks.
→ 01
Scope what's real
We sit with your team, read your code, and separate the AI feature you want to ship from the AI feature your investors retweeted.
→ 02
Architect the boring parts
Routing, fallbacks, evals, observability. Decisions made now that you won't have to reverse in six months.
→ 03
Ship in production-grade increments
Sprints with measurable evals at the end of each one. No demos behind a feature flag forever.
→ 04
Hand off or stay embedded
Retainer, embedded engineer, or full handoff with runbooks. Your call — we don't sell lock-in.
// 01
Multi-model
No single-vendor lock-in
// 02
Continuous evals
Quality measured, not assumed
// 03
Production-first
Guardrails, traces, retries
// 04
Embedded
We work inside your team
[ 05 ]// FAQ
Questions buyers actually ask before signing.
Q.01 How is this different from an AI studio or agency?
Studios sell demos and decks. We're the technical team that ships the infrastructure underneath — routing, evals, agents, ingestion — and stays accountable to the production traces.
Q.02 Do you lock us into a specific model provider?
No. Multi-model architecture with provider routing and fallbacks is our default. If a vendor changes pricing or deprecates a model on a Friday, your product is still up on Monday.
Q.03 What engagement models do you offer?
Three: scoped sprints for a defined deliverable, monthly retainers for ongoing infrastructure work, and embedded engineers who join your team for a quarter or more.
Q.04 How do you measure that an LLM feature actually works?
Continuous evals against your real data, latency and cost dashboards, and trace-level observability. We agree on the metric before we start, not after the launch.
Q.05 Can you take over an existing AI project?
Yes. Most of our work starts with a prototype someone else built that needs to survive real users. We audit, harden, and hand back something you can actually run.
[ 06 ]// Contact
Tell us what you're building.
We reply in under 24 hours. If it fits, we schedule discovery the following week.