Vero logoVERO

AI agent reliability partner

Ship agents you can measure.

For teams shipping agents into real workflows (HubSpot/Zendesk/Jira/Slack/internal APIs). We turn real usage into a clear agent spec, a baseline scorecard, and CI-ready evals-so improvements are measurable and don't regress.

Book a 20-minute call

Implemented for you-delivered as JSON + Markdown reports and CI checks in your repo.

Built for agents that call real tools

Delivered in your repo (CI-ready)

Before/after proof, not vibes

Agent scorecard

Revenue Ops Copilot

Internal RevOps analyst assistant

ROI Index

7.4

Success rate
68%
Tool failures
19%
Avg latency
42s
Cost / success
$0.31

Focus workflows

Lead enrichment & routing

High

74% pass rate - Field rename in enrichment tool caused 22% of requests to drop required firmographic data.

Contract amendment drafting

High

61% pass rate - Tool retries hide schema drift; signature blocks sometimes missing legal entity.

Full scorecard lives in CI (downloaded as JSON + Markdown report).

The problem

Agent teams ship blind without requirements grounded in real usage.

Agent ROI stalls when teams can't define 'done' from real usage - or prevent regressions after prompt/model/tool changes. Vero extracts requirements from traces and converts them into tests your engineers can run (golden traces + contract checks + CI gate).

  • Regressions after prompt/model/tool changes
  • Tool call failures (schema drift, retries, rate limits)
  • Costs rise without performance signals
  • Debugging is guesswork (no blame isolation)
  • Shipping becomes risky and slow

Best fit: an agent already in use (or about to ship) that touches real tools-where reliability, cost, and regressions are hurting momentum.

How it works

Clarity without extra homework.

Provide any one: a staging endpoint, a repo link, or a handful of logs/tickets. No PRD required.

Step 1

Clarify

We turn messy goals + real usage into a 1-page agent spec (what it should do, what it must not do, success metrics, handoff rules).

Step 2

Measure

Baseline success rate, tool failure rate, cost/latency per successful task, and the top failure patterns pulled from actual traces.

Step 3

Improve + lock in

Fix the highest-ROI issues and convert workflows into regression evals + tool contract tests so improvements don't regress.

Ideal when there's a technical owner (Eng/ML/Agent lead) and a business owner (Ops/Product) who both care about outcomes.

Week 1 ends with a scorecard + first regression suite merged into your repo.

Deliverables

Concrete assets every time.

Everything lands in your repo or CI-no mystery playbooks.

  • Agent Performance Scorecard (baseline + ROI-ranked fixes) - 3-5 days.
  • Before/After Report - delivered with the regression suite.
  • Golden-trace Regression Suite (CI gate with red/green diffs) - week 1-2.
  • Tool Contract Tests (schema/required fields/retry/idempotency/invariants) - as needed, week 1-2.

Engagements are fixed-scope sprints or monthly retainers.

Designed for teams who ship changes weekly and need a safety net: measurable quality, stable tool behavior, and predictable cost per successful task.

Mini-FRED case study

Mini-FRED: Proof of evaluation rigor

A reference benchmark showing how Vero-style evaluation improves reliability across versions.

This is a benchmark scorecard, not a live system claim.
MVES (Minimum Viable Evaluation Suite) is the smallest set of tests that captures real-world improvement. Vero installs MVES directly in your repo with deterministic checks and regression cases.
Mini-FRED uses 5 years of FRED data to answer finance questions with deterministic transforms. It's an offline benchmark, not a claim about a live system.
Result: 65.9% -> 78.2% (+12.3 pts) with transparent failure breakdowns and CI-ready reports.

Executive summary

  • Reliability improved from 65.9% (v1) to 78.2% (v5), a +12.3 point lift.
  • Primary remaining issue: Transform confusion (intent parsing drives the wrong computation path).
  • Next improvements focus on rules-first transforms, a stricter output contract, and clarifying questions for ambiguous phrasing.
ProgressionPass rate
v1
65.9%
v2
69.8%
v3
69.8%
v4
66.8%
v5
78.2%

Offline MVES benchmark • 560 questions

Mini-FRED Agent v1

Baseline deterministic agent scored against DuckDB-grounded truth.

Benchmark Index (proxy)

657.4

Success Rate

65.9%

Critical flags (assertion)

441

Pass / Fail

369 passed

191 failed

Top failure modes

Wrong computation path · 181Transform confusion · 142Output numeric formatting · 60

Baseline parser + deterministic DuckDB truth checks.

BenchmarkIndex = round((passRate * 10) - (criticalFailures/totalCases) * 2, 1)

1 / 6Agent version

Mini-FRED Agent v1

Baseline deterministic agent scored against DuckDB-grounded truth.

Executive takeaway: Baseline reliability with clear transform errors.

What changed

  • >Baseline parser + deterministic DuckDB truth checks
  • >Single-pass transform selection
  • >No intent normalization yet

Primary remaining issue

Wrong computation path (misidentifies the requested transform, so correct data but wrong calculation).

Offline MVES benchmark • 560 questions

Mini-FRED Agent v2

Improved parsing to reduce date handling and extraction errors.

Benchmark Index (proxy)

696.8

Success Rate

69.8%

Critical flags (assertion)

339

Pass / Fail

391 passed

169 failed

Top failure modes

Wrong computation path · 159Transform confusion · 120Output numeric formatting · 60

Improved date parsing + stricter value extraction.

BenchmarkIndex = round((passRate * 10) - (criticalFailures/totalCases) * 2, 1)

2 / 6Agent version

Mini-FRED Agent v2

Improved parsing to reduce date handling and extraction errors.

Executive takeaway: Higher success rate; computation path still dominant.

What changed

  • >Improved date parsing + stricter value extraction
  • >More explicit transform coercion
  • >Cleaner numeric extraction fallback

Primary remaining issue

Transform confusion remains (better parsing, but still picks YoY vs MoM incorrectly in noisy phrasing).

Offline MVES benchmark • 560 questions

Mini-FRED Agent v3

Retrieval + parsing refinements to stabilize answer formatting.

Benchmark Index (proxy)

696.8

Success Rate

69.8%

Critical flags (assertion)

339

Pass / Fail

391 passed

169 failed

Top failure modes

Wrong computation path · 159Transform confusion · 120Output numeric formatting · 60

Retrieval + parsing refinements; stabilized outputs.

BenchmarkIndex = round((passRate * 10) - (criticalFailures/totalCases) * 2, 1)

3 / 6Agent version

Mini-FRED Agent v3

Retrieval + parsing refinements to stabilize answer formatting.

Executive takeaway: Stability improved, but core failure types persist.

What changed

  • >Retrieval + parsing refinements; stabilized outputs
  • >Reduced ambiguous transform collisions
  • >More consistent series selection

Primary remaining issue

Wrong computation path persists (formatting stabilized, but intent errors still dominate).

Offline MVES benchmark • 560 questions

Mini-FRED Agent v4

Guardrails added for windows, dates, and refusal correctness.

Benchmark Index (proxy)

666.6

Success Rate

66.8%

Critical flags (assertion)

394

Pass / Fail

374 passed

186 failed

Top failure modes

Wrong computation path · 176Transform confusion · 120Output numeric formatting · 78

Window/date guardrails; more refusal correctness.

BenchmarkIndex = round((passRate * 10) - (criticalFailures/totalCases) * 2, 1)

4 / 6Agent version

Mini-FRED Agent v4

Guardrails added for windows, dates, and refusal correctness.

Executive takeaway: Guardrails helped, but wrong paths still frequent.

What changed

  • >Window/date guardrails; more refusal correctness
  • >Refusal criteria made explicit
  • >Tighter date-window matching

Primary remaining issue

Edge-case date parsing (improved windows/Moving Average, but some ambiguous date prompts still fail).

Offline MVES benchmark • 560 questions

Mini-FRED Agent v5

Local Phi-4 intent normalization reduces transform ambiguity.

Benchmark Index (proxy)

780.9

Success Rate

78.2%

Critical flags (assertion)

301

Pass / Fail

438 passed

122 failed

Top failure modes

Transform confusion · 128Wrong computation path · 119Output numeric formatting · 54

Local Phi-4 intent normalization for transform detection.

BenchmarkIndex = round((passRate * 10) - (criticalFailures/totalCases) * 2, 1)

5 / 6Agent version

Mini-FRED Agent v5

Local Phi-4 intent normalization reduces transform ambiguity.

Executive takeaway: Best reliability so far; transform confusion remains.

What changed

  • >Added local Phi-4 intent normalization for transform detection
  • >Reduced transform ambiguity on edge phrasing
  • >More consistent transform labels downstream

Primary remaining issue

LLM still misses some nuance (Phi-4 helps, but certain change vs level cues still misfire).

Planned improvements roadmap

What's next

Potential upgrades focused on transform clarity and output contracts.

Benchmark Index (proxy)

—

Success Rate

—

Critical flags (assertion)

—

Pass / Fail

—

Focus areas

Transform clarity & output contracts

BenchmarkIndex = round((passRate * 10) - (criticalFailures/totalCases) * 2, 1)

6 / 6Roadmap

What's next

Potential upgrades focused on transform clarity and output contracts.

What changed

  • >Rules-first transform lexicon (map phrases like "annual swing" -> YoY, "month-to-month" -> MoM)
  • >Output contract: enforce {series_id, transform, date/window, value} and single numeric
  • >Confidence gate: ask a clarifying question when date/window/transform is ambiguous
  • >Add more transforms step-by-step later (avg, median, CAGR, z-score)
  • >Expand eval coverage for ambiguous phrasing + adversarial paraphrases

Focus

Transform ambiguity + output contract discipline

View case study →GitHub repo

Benchmark Index is an internal proxy (not ROI).

The point: the same eval workflow applies to support, RevOps, and internal copilots-any agent that must be correct, stable, and cheap enough to run.

Founder

Madhur Srivastava

Systems-focused engineer and technical founder with deep experience building performance-critical trading technology-now applying the same rigor to evaluation-driven AI.

I build AI that behaves like real software: instrumented, testable, benchmarked, and improved with data.

Why work with me

  • >Production-grade engineering: distributed systems, observability, and performance tuning, so evals run reliably in real CI/CD.
  • >Evaluation-first delivery: ground truth, regression tests, and scorecards, so improvements are provable, not subjective.
  • >Founder-led engagement: one accountable owner, fast iterations, clear communication, and predictable handoff.

Background

Built and operated low-latency trading systems where small errors are expensive. That is why Vero emphasizes contracts, regression tests, and measurable reliability.

If we can't identify 3 concrete improvement opportunities in the first review, you won't be charged for the review.

Contact

Tell me about your agent.

Start with whatever is easiest: staging link, repo access, or a few sanitized examples. No PRD required.

Staging/VPC-friendly if needed.

Email: madhur@hivesoft.aiBook a call

Address: 215 W Superior St. Suite 700, Chicago IL