CAPABILITIES

Evaluation & Reliability

Eval harnesses, regression suites, and monitoring so your AI behaves tomorrow the way it did in the demo: BFCL, TAU-bench, and DeepEval harnesses, multi-LLM comparison, hallucination reduction. Built into everything we ship; available standalone.

PRACTICE NO.
09
PRINCIPALS
PA · NB
CASES ON FILE
00
FIELD
AI / ML INFRASTRUCTURE · SAAS & DEVELOPER TOOLS · CONVERSATIONAL AI · NLP & DOCUMENT AI
Evaluation Reliability
FIG. 01 · NEIGHBORHOOD MAP · SOURCE: TEAM EXPERTISE DATASET

PROBLEM SPACE

The discipline that makes AI safe to run in production.

Eval harnesses, regression suites, and monitoring so your AI behaves tomorrow the way it did in the demo: BFCL, TAU-bench, and DeepEval harnesses, multi-LLM comparison, hallucination reduction. Built into everything we ship; available standalone.

ROUTINE CLEARS AUTOMATICALLYEDGE CASES ROUTE TO A REVIEWER

WHAT WE DELIVER

  • 2.1eval harnesses
  • 2.2multi-LLM benchmarking
  • 2.3hallucination reduction
  • 2.4monitoring & alerting
  • 2.5CI gates for AI behavior
PRODUCTION-SAFE BY DESIGN

HOW IT SHIPS

Systems in this practice run as a loop, not a launch. Inputs are evaluated, routine outcomes execute automatically, and anything consequential holds at a review gate where a named person clears it with context attached. Every decision (human or automatic) lands in the audit trail.

INPUTEVALUATEGATEEXECUTEAUDITHUMAN REVIEW · CONSEQUENTIAL ONLY
THE ENGAGEMENT MODEL BEHIND THIS →

PROOF

3
public eval harnesses run (BFCL · TAU-bench · DeepEval)AI REASONING-INFRA CO

NO PUBLIC CASE FILE · FIGURES SOURCED FROM ENGAGEMENT RECORDS

SELECTED PROJECTS · CLIENTS ANONYMIZED

One pipeline for text, audio, image, and videoA generative-AI automation startupGenerated code, audited before it shipsA smart-contract developer-tools companyAn evaluation harness that catches the model outAn AI reasoning-infrastructure company
EVERY FIGURE TRACES TO SHIPPED WORK

WHO BUILDS IT

Parik AhlawatPrincipal · Agents & OrchestrationMulti-agent platforms, model routing, n8n orchestration. 618+ automations shipped across 12 industries.PROFILE →Nikhil BeryPrincipal · RAG & Document AIRAG, document intelligence, multi-LLM pipelines, vector + graph search at scale. 30M-doc Elasticsearch indexes.PROFILE →

FIELD NOTES

Private AI, in-house: the shift to internal models and agentic infrastructure6 MIN READFraud screening at 16 million decisions a month: what holds5 MIN READThe AI app you ship in eight weeks: what production-ready means5 MIN READHuman-in-the-loop is a feature, not a disclaimer4 MIN READWhy most AI never reaches production5 MIN READ

ADJACENT

Asked about this practice

Questions we get.

The same answers we give on a first call about this practice.

  • FAQ-01Why is evaluation its own practice?

    Because most AI dies in the gap between a demo and a system that behaves the same tomorrow. We build eval harnesses, regression suites, and monitoring so behavior is measured, not assumed. It is built into everything we ship and available on its own.

  • FAQ-02How do you actually measure whether the AI is working?

    With a defined eval set, public harnesses like BFCL, TAU-bench, and DeepEval, multi-LLM benchmarking, hallucination reduction, and CI gates for AI behavior. The absence of complaints is a silence, not a signal, so we replace it with real measurement.

  • FAQ-03Can you evaluate a system we already run in production?

    Yes. The practice is available standalone: we wrap monitoring, alerting, and a regression suite around an existing system so a model update or a new edge case surfaces as a caught test, not a 2am incident.

OPEN A NEW FILE

Tell us the use case. One call is enough to scope whether there is a fit, and what it takes to ship.

Start a brief