AI & Tech
Technology Approach
Model-agnostic, tool-driven, and observable. We build agentic systems that move with the frontier — without locking clients into any single vendor.
Our Philosophy
Three principles that shape every system we build
The frontier of AI is moving fast. The model that's state-of-the-art today will be commodity in eighteen months. Our job isn't to bet on a single horse — it's to build operational architectures that absorb new capabilities as they arrive.
That requires a specific kind of discipline. Loose coupling between models and orchestration. Tools that abstract over providers. Evaluation harnesses that catch regressions before they hit production. And telemetry deep enough to debug an autonomous agent at 3am.
"The best AI architecture is the one you can swap pieces of without rewriting the whole system."
Core Principles
Built to absorb the frontier, not chase it
Model-agnostic
We treat foundation models as interchangeable components. Anthropic, OpenAI, open-weight, fine-tuned — every system can route across providers per task.
Tool-driven
Agents earn their value from the tools they wield, not the prompts that wrap them. We invest in robust, well-tested tool surfaces over clever prompting.
Observable
Every decision an agent makes is captured, traced, and replayable. If you can't see it, you can't trust it — and you certainly can't debug it.
Stack at a glance
12+
Foundation models routed in production today
100%
Of agent runs captured in our trace store
<200ms
Median tool-call dispatch latency
Orchestration
Where custom logic meets open standards
Our orchestration layer combines a custom-built agent runtime with carefully selected open source primitives. We avoid heavyweight frameworks that bury control flow — preferring code that engineers can read, debug, and extend.
Custom agent runtime
A typed, deterministic execution engine for agent loops. Handles retries, timeouts, tool dispatch, and state checkpointing — without the magic of opaque frameworks.
Open source primitives
We integrate vetted libraries — Inngest for durable workflows, OpenTelemetry for traces, Pydantic for structured I/O — instead of reinventing well-solved problems.
Provider routing
A policy-driven router decides which model serves which step — balancing latency, cost, and capability. Swappable per-tenant, per-task, per-deployment.
Memory & context
Episodic memory, semantic retrieval, and tenant-scoped knowledge bases. Agents remember without leaking context across boundaries that matter.
Evaluation
We measure agents the way you'd measure a hire
Outcome-based suites
Test sets are built from real production tasks, scored on the business outcome — not just whether the model emitted plausible text.
LLM-as-judge with humans
We use model-graded eval for scale, calibrated against human review samples to catch the cases where the judge disagrees with reality.
Regression gates in CI
No prompt change, model swap, or tool update reaches production without passing the eval suite. Drift is caught at the pull request, not in the wild.
Adversarial probes
Red-team prompts, jailbreak attempts, and out-of-distribution inputs are run continuously. Safety isn't a launch checklist — it's a heartbeat.
Observability
If you can't trace it, you can't trust it
Every prompt, tool call, retrieval, and decision flows into a unified trace store. Operators can replay any agent run, inspect every intermediate state, and pinpoint exactly where reasoning diverged from expectation.
Distributed tracing
OpenTelemetry-native spans across LLMs, tools, queues, and downstream services. One trace, end to end.
Cost & latency budgets
Per-tenant, per-workflow ceilings with automatic alerting. Runaway agents get throttled before they get expensive.
Replay & rewind
Reproduce any historical agent run with a single command. Essential for debugging, audits, and post-incident review.
Want to see the architecture under the hood?
Book a discovery call and we'll walk you through how our stack maps to your operational reality.
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