Evaluation and observability loop
Golden sets, judges, traces and alerts, wrapped around every other architecture.
Use it when
Always. It is how we know whether a change made the system better, and how you know before your customers do.
The parts, top to bottom
across every level
Hover or tap any part to see what it does. The light shows the order a request moves through.
What happens, in order
- 1Collect real inputs
- 2Label expected outputs
- 3Run evals in CI on every change
- 4Trace production calls
- 5Sample & score online
- 6Alert on drift, feed back to the golden set
What we typically build it with
Trade-offs
Costs a seed set of 20–50 real cases, growing to 50–200 per route, plus 50–100 held-out examples to calibrate judges (below 80% agreement with humans a judge is not trusted). It pays for itself the first time a model upgrade quietly changes behaviour.
Problems this architecture solves
Generic problem statements with the flow and the outcomes the industry has documented.
Support queues that never shrink
Tier-1 questions consume most of the team's day, answers vary by agent, and customers wait. Generic chatbots deflect badly because they do not know your policies or your systems.
How the system works
- Customer message
- Intent & policy check
- Retrieve from your docs & orders
- Answer or act via tools
- Escalate with context
Outcome: Klarna's assistant handled two thirds of customer chats in its first month and cut resolution time from 11 minutes to under two; Intercom reports Fin resolving 76% of conversations on average. Klarna press release
ArchitectureDocuments keyed in by hand
Invoices, KYC files, claims and contracts arrive as PDFs and photos. People retype them, errors slip through, and the backlog grows every month-end.
How the system works
- Ingest PDF / image / email
- Classify document type
- Extract to a schema
- Validate & score confidence
- Auto-post or route to review
Outcome: Documented deployments classify documents in under a second, turn day-long claims backlogs into minutes, and reach 85% no-touch invoice processing within six months with a seven-month payback. DXC with Claude; Vic.ai
ArchitectureAnswers buried in ten systems
Policies live in SharePoint, decisions in email, procedures in Confluence and history in tickets. New staff take months to become useful and experts answer the same questions daily.
How the system works
- Connect sources
- Chunk with context, embed + index
- Hybrid retrieval + rerank
- Cited answer with permissions
- Feedback into evals
Outcome: Contextual chunking with hybrid search and reranking cuts retrieval failures by 67% versus plain vector search, which is the difference between an assistant people trust and one they abandon. Anthropic: Contextual Retrieval
ArchitectureAI features that quietly get worse
A prompt tweak fixes one case and breaks three others. A provider upgrades a model and answers change tone or accuracy. Nobody notices until customers do.
How the system works
- Golden set from real traffic
- Offline evals in CI
- Release gate on thresholds
- Traces sampled online
- Failures fed back to the set
Outcome: Regression evals target near-100% pass on known cases before every release, with judges calibrated against human reviewers and gates that block a drop of more than a few points. Anthropic: Demystifying evals for AI agents
ArchitectureOther platform patterns
Model routing, caching and cost gateway
One door for every model call: routing, caching, budgets and fallbacks.
Use it when: As soon as more than one team or feature calls models, or when cost and latency need to be engineered rather than hoped for.
Flow
- 1Request with intent & budget
- 2Semantic / exact cache check
- 3Route: small model first, escalate if needed
- 4Prompt caching & batching
- 5Fallback provider on error
Let's build intelligent systems that drive growth
Tachyon is the engineering partner for teams that need AI in production, not in a deck. Start with a free 60-minute discovery call.
Tell us the problem, we will map it to the architecture
Every great partnership begins with a conversation. Whether you are exploring possibilities or ready to scale, tell us what you are actually trying to build.
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