SYS · ONLINEID-04.AX
[01]Intelligence layer
Intelligentsystemsfor real ops
We design, ship, and govern AI that sits inside products—models, pipelines, and interfaces teams can trust in production.
Latency42ms
- RAG architectures
- Agent workflows
- Eval harnesses
- Model routing
- Guardrails
- Observability
[02]Signal
Built for teams that ship models—not decks
Most “AI strategy” dies in slides. We wire intelligence into product surfaces, data layers, and ops rituals so impact is measurable every release cycle.
Problem space
Unreliable copilots, opaque model choices, and missing evals that block scale.
What we ship
Reference architectures, production paths, monitoring, and team playbooks.
Who it’s for
Product, data, and platform leads inside AI-native or modernizing companies.
[03]Modules
Four layers we assemble
Pick a lane or stack the full vertical—from retrieval to runtime governance.
01
Knowledge & retrieval
Chunking strategies, hybrid search, citation-safe RAG so answers stay grounded in your corpus.
02
Agents & orchestration
Tool use, multi-step plans, and fail-soft loops that behave under load—not demos that break after hop three.
03
Evals & quality gates
Offline + online evaluation harnesses, golden sets, and promotion criteria wiring to CI and release trains.
04
Platform & guardrails
Routing, cost controls, red-team patterns, observability—everything between a prototype and an SLA.
[04]Pipeline
How intelligence moves from brief to runtime
Frame
Map use cases, data rights, risk surface, and success metrics with product + legal in the room.
Week 1–2Prototype
Thin slices, synthetic evals, early UX for trust signals—prove signal before platform spend.
Week 3–5Harden
CI evals, tracing, fallbacks, cost budgets, and red-team passes wired to deployment gates.
Week 6–9Launch
Canary traffic, runbooks, on-call paths, and handover kits engineering owns next.
Week 10–12Scale
Model routing, multi-region, fine-tune options, and product experiments against live KPIs.
Week 12+[05]Output package
What lands in your repo
Artifacts engineering can review, extend, and audit—not a zip of notebooks named final_v7.
- 01
System map
Architecture diagrams, data contracts, and threat notes for security reviews.
- 02
Working branch
Reference implementation with stubs for tools, model adapters, and config layers.
- 03
Eval suite
Datasets, scorers, and CI hooks so quality does not drift after handoff.
- 04
Ops kit
Dashboards, alert thresholds, incident runbooks, and cost budgets.
- 05
Enablement
Workshops for product, eng, and support so the system keeps improving in-house.
[06]Engage
Ready to put AI on a production schedule?
Share your stack, constraints, and the job the model should do. We’ll return a framed approach within a few business days.
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