Senior-led, every accountReviews published with real names
5.0
from 60 published client reviews
Senior pod, no juniors
on every account
Brands across the industries we serve
What is Agent Evals & Monitoring?
The safety layer: eval suites, guardrails, tracing and cost/latency monitoring that keep agents trustworthy in production.
Discipline: AI Agents
Typical outcome: Evals quality quantified
Engagement: monthly retainer or one-time audit
Seniority: lead strategist on every weekly call
About This Service
How We Deliver Agent Evals & Monitoring
You cannot ship what you cannot measure. We build eval suites tied to real failure modes, input/output guardrails, PII and jailbreak checks, full request tracing, and dashboards for cost, latency and quality — plus regression gates so a prompt change can’t silently break behaviour.
No retainers without a measurable outcome attached. Every sprint is anchored to a KPI we agreed on in week one — with weekly Loom updates, a shared dashboard, and a senior strategist on every call.
Senior strategist on every account — no juniors learning on your dime
Weekly Loom updates plus a shared Looker dashboard
Your stack, your CMS, your CMS workflow — we adapt to you
Outcome-tied pricing — KPI ranges baked into the contract
Our Process
How we ship Agent Evals & Monitoring
A proven 4-step framework that compresses time to results without cutting corners.
01
Define quality
We build task-based eval suites that quantify what "good" means for your agent.
02
Catch drift
Production monitoring and observability detect regressions and drift before users do.
03
Watch safety
Continuous safety and guardrail checks catch unsafe or off-scope behaviour.
04
Improve on evidence
Version-over-version comparison so the agent improves on measurement, not opinion.
You can't improve an AI agent you can't measure — and most teams have no definition of "good". We build task-based evaluation suites that measure your agent's quality against real tasks and criteria, so agent performance is quantified, comparable across versions, and improvable rather than a matter of opinion.
Rigorous evals are the foundation of every reliable AI system — they turn "seems fine" into measured quality.
Task-based evaluation suites
Quality metrics against real criteria
Version-over-version comparison
Quantified, improvable agent quality
02
Typical shape of an engagement
Months 4–9 is where money keywords move
Catch drift & regressions
Know before your users do
AI agents degrade silently as data, models and usage change — and un-monitored, you find out from angry users. We build monitoring and observability that track quality, cost and behaviour in production, and alert on regressions and drift, so you catch problems before they reach customers.
Continuous monitoring is what keeps a deployed agent reliable instead of quietly rotting.
Production quality and behaviour monitoring
Drift and regression detection
Cost and performance observability
Alerting before users are affected
03
Content velocity
Briefed, drafted, edited, shipped
Safety & guardrail checks
Catch unsafe behaviour continuously
An agent in production can produce harmful, off-scope or non-compliant outputs, and unmonitored these slip through. We build safety monitoring and guardrail checks that continuously watch for unsafe behaviour, policy violations and edge-case failures, so risky outputs are caught and addressed rather than discovered after harm.
Ongoing safety monitoring is what makes an AI agent something you can responsibly keep in production.
Continuous safety and policy monitoring
Guardrail and edge-case-failure detection
Incident detection and response
Responsible, monitored production operation
How we work
Agent Evals & Monitoring — what you are actually buying
Commitments rather than results: how the engagement is staffed, reported and priced. Every one of them is true on the first day, before anything has been measured.
Named senior specialists on every engagement
Reported via live Looker Studio dashboards
Tied to revenue KPIs, not vanity metrics
Evals
Quality quantified
Drift
Caught before users
Safety
Continuously monitored
Improvable
Evidence-based
FAQs
Questions buyers ask before signing
Quick answers to the things buyers always check first.
Why do AI agents need evaluation and monitoring?
Because an agent you can't measure or watch is a black box that drifts and fails silently. Evals define and quantify quality so you can improve it; monitoring catches regressions, cost spikes and unsafe behaviour in production before users do. It's what makes an AI system trustworthy and maintainable.
What are "evals"?
Task-based evaluation suites that measure your agent's quality against real tasks and criteria — so performance is quantified and comparable across versions, rather than a matter of "seems fine". They're the foundation of every reliable AI system.
How do you catch quality drift?
With production monitoring and observability that track quality, cost and behaviour continuously, plus regression and drift detection with alerting — so you're warned before degradation reaches customers, not after.
Can you add this to agents we already have?
Yes — we can build evaluation and monitoring around existing AI agents and features, giving you the observability, quality measurement and safety checks to trust, maintain and improve what's already deployed.
No account-manager buffer and no juniors learning on your budget — you work directly with the people who do the work.
Founder
Rao Usama
Co-Founder & Product Officer, Head of AI / Data
14+ yrs engineering · AI agents, MCP & automation
Co-Founder and Head of AI & Data. 14+ years in software engineering; leads our AI-agent, MCP-integration and workflow-automation work — turning models into systems that actually run in production.
MSc, Data Science
Abdur Rahman Shah
Web & SaaS Engineer · DevOps
WordPress · custom web apps · SaaS · DevOps · technical SEO
Builds and ships the web layer — WordPress, custom web apps and SaaS architecture — with the DevOps to run it and the technical-SEO knowledge to make sure what he builds is fast, crawlable and built to rank.
Describe the queue and we will tell you honestly whether an agent can take it safely, what it would need access to, and where a human still has to sit in the loop.
What clients say about our Agent Evals & Monitoring
Reviews from our Upwork, Fiverr and direct client engagements. Each card shows where the review was left.
5 avg. client review · 60+ engagements
Slide 1 of 8
A lot of moving parts, kept organized from start to finish. Clear communication, reliable technical direction, and every deadline met without delays.
Daniel Brooks
SaaS growth lead
Slide 2 of 8
Structured, reliable, and clear. They explained the process, responded quickly to questions, and completed everything according to plan.
Evan Parry
Real-estate marketing lead
Slide 3 of 8
They compressed what we expected to be a nine-month sandbox into a couple of months. The authority plan and outreach stack were exactly what a brand-new site needed.
Filip Gavrilovski
iGaming acquisition lead
Slide 4 of 8
Well-researched, well-structured content that aligned perfectly with our goals. Organized, easy to collaborate with, and consistently on time.
Holly Caunt
Content lead
Slide 5 of 8
Our traffic had been flat for a long time. Their strategy was well thought out and we started seeing real momentum soon after implementation.
Jack Evans
Startup founder
Slide 6 of 8
The technical audit uncovered issues we had no idea about. Clear explanations, practical recommendations, and a report that told us exactly what to fix first.
James Williams
Law-firm marketing lead
Slide 7 of 8
Understood the brief after a single call and executed with confidence. Delivered on schedule with excellent communication the entire way through.
Jenny Abouobaia
Agency owner
Slide 8 of 8
They consistently identify high-quality opportunities, keep communication clear, and deliver exactly what we need. A partnership we plan to keep.
Jessica Levenson
Marketing director
Contact
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