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Evals & Reliability

Evals, LLMOps & Reliability

The apparatus that turns a promising LLM system into one you can operate: eval suites, retrieval quality, trace capture, cost controls, and the release gates that decide what ships.

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Release Gate

Instrumented

Candidate build

Eval Harness

score · compare · gate

Golden paths

Known-good cases, scored every run

Adversarial set

The failures that would cost you

Cost & latency

Budget per workflow, p95 tracked

Traces

Every step replayable after the fact

What this delivers

A release passes the gate or it does not ship.

Timeline
4-8 weeks
Engagement
AI-native engineering
Output
Eval harness in CI
Start with
A scoped engagement

Who It's For

Teams whose LLM system needs reliability, measurement, and an owner.

  • Quality varies across prompts, documents, users, or edge cases and nobody can say why
  • Traces and evals are incomplete, so failures are explained only in hindsight
  • Cost, latency, or model dependency risk is blocking a launch
  • A retrieval prototype did not meet quality expectations and needs a rebuild

Our Approach

Delivered by a forward-deployed team on agreed milestones, with a monthly retainer to run it after launch.

1

Baseline behavior

Week 1

Capture current quality, latency, cost, retrieval behavior, and failure patterns. You cannot gate a release against a number nobody has measured.

2

Design the evals

Weeks 2-4

Build test sets, scoring methods, golden paths, adversarial cases, and the thresholds a release has to clear. Evals are written against the failures that would cost you something.

3

Harden the architecture

Weeks 5-7

Improve retrieval, model routing, caching, guardrails, and rollout controls. Add trace capture so runtime behavior is debuggable from the trace.

4

Operationalize

Week 8 (example)

Wire evals into CI, stand up dashboards, alerting, and runbooks, and hand over the ownership practices that keep quality from drifting after we leave.

Retrieval Quality

When a system answers from a corpus, retrieval quality constrains the evidence available to the model. Inspect and measure that path as part of the whole system.

Chunking and ingestion

Chunking and embedding strategies matched to your content types, with an ingestion pipeline that connects to the document stores you already run.

Hybrid retrieval

Semantic and keyword retrieval together, with reranking and citations, because pure vector search degrades quietly on enterprise corpora.

Retrieval evals

Automated tests for retrieval relevance, answer accuracy, and faithfulness, running as regressions so quality is monitored continuously.

Failure diagnostics

Inspect ingestion, chunking, retrieval, ranking and evaluation coverage before deciding what needs rebuilding.

Common Questions

We already have LLMs in front of real users. Can you add evals without disruption?

Yes. We start with observability through logging and tracing, then layer automated evals alongside your existing systems without changing live code paths.

Can you audit an existing system rather than build a new one?

That is the common case. An audit produces a hardening roadmap with the failure modes ranked by what they would cost you in front of real users.

Do you only work on retrieval systems?

No. Retrieval is common, and we also work on tool-using agents, classification, extraction, copilots, and workflow automation.

How do you handle prompt and configuration versioning?

Versions are tracked, linked to their eval results, and wired into the deployment pipeline. That gives you an audit trail and a fast rollback.

What cost reduction is realistic?

It depends on your current architecture. The usual levers are model routing, caching, prompt reduction, and eliminating redundant calls. Spend is instrumented first, so the savings are measured.

What You Get

  • Eval suite covering quality, regression, cost, and latency
  • Golden paths, adversarial cases, and release gates
  • Trace capture and observability pipeline
  • Retrieval quality diagnostics where retrieval is in play
  • Cost monitoring, model routing, and caching strategy
  • Prompt and configuration versioning tied to eval results
  • Dashboards, alerting, and incident runbooks
Discuss your project

AI-Native SaaS / selected work

Agent Platform Engineering for Enterprise AI

The problem
Give AI agents a way to find, create and update a content platform's structured information.
What we built
Backend AI with Anthropic, OpenAI and Google models, 50+ MCP tools, content APIs and semantic search.
What it made possible
Agents that find information, act on it and power interactive experiences inside compatible AI clients, for a unicorn startup and Fortune 500 companies.
50+
MCP tools built

Backend AI, API and infrastructure engineering, from architecture through launch.

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Agent platform engineering

  1. Backend AIAnthropic, OpenAI and Google
  2. 50+ MCP tools builtDiscover, create and update content
  3. APIs and searchStructured content and semantic retrieval
  4. Platform infrastructureSupports agents and MCP Apps
The platform's agent capabilities, layer by layer.

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