Your AI agent ships every day.
So do the next ways it can break.
We test the agents that test the systems running banks, federal agencies, and Fortune 500 customer ops. One assessment. Four numbers. An answer your CFO can take to the next board meeting.
Start with a product.
Expand to custom.
AI Voice Agent
2-4 weeksHandles inbound and outbound calls, qualifies leads, books meetings. 24/7, in your brand's voice.
View case study →AI Lead Intelligence
2-3 weeksScans, scores, and enriches leads. Research time drops from 15-20 hrs/week to 2-3.
Get started →Custom Agent Build
6-12 weeksEnd-to-end agent development on any stack. Salesforce, custom APIs, open-source models. Your infrastructure, our engineering.
Get started →We build with Claude, OpenAI, Gemini, and open-source models. Every agent is tailored to your operations, your data, your stack. Salesforce teams? See our Agentforce practice.
The numbers your CFO
actually asks for.
Every AI engagement collapses to four questions. The Scoping Assessment populates each with anonymized math from real production engagements, so the answer is defensible at the next finance review.
Hours x Rate
Loaded labor hours saved per week, multiplied by your loaded internal rate.
Voice agent fields 220 calls/week. 6 minutes saved per qualified call at $95 loaded rate. ~$108k/year.
Errors x Revenue
Defect-driven revenue leakage avoided. (Errors per period) x (revenue at risk per error).
Document agent reduced misrouted contracts from 4.2/week to 0.3/week at $1,800 each. ~$365k/year.
Cycle x Throughput
Time-to-decision compression x volume of decisions. Where automation either shortens the cycle or lets the same team take more swings.
Lead-intelligence agent cuts research-to-meeting from 4.2 days to 1.1. Same SDR headcount, +38% pipeline.
Ramp x Cost
Onboarding-to-productivity timeline x fully-loaded cost of the seat. Where an agent absorbs the bottom of the work.
New CSR ramp drops from 11 weeks to 4 because the agent handles tier-0 questions. ~$26k saved per hire.
Automation that
compounds value.
Sales & Revenue
Lead qualification, outreach sequencing, pipeline management, and revenue forecasting.
Customer Service
Intelligent routing, resolution, and escalation. 80%+ of tickets handled autonomously.
Document Processing
Extraction, classification, and routing. Contracts, invoices, compliance docs, intake forms.
Workflow Orchestration
Multi-step automation across Jira, Salesforce, HubSpot, Asana, and custom APIs.
AI Governance
NIST AI RMF aligned. Audit logging, decision traceability, human-in-the-loop controls.
Built in production.
Available for yours.
GoOmni is our agent platform: indexed knowledge bases, shared concepts and ontology so agents stay aligned, pre-built agent patterns for common workflows, and voice and phone agents that scale with traffic. The same core supports compliance automation, QA copilots, lead intelligence, and orchestration. We run it as a managed deployment, integrate it with your stack, or extend it with custom work.
Compliance Automation
ADA auditing, sensitive data detection, document remediation, and regulatory reporting. Built for regulated industries.
AI-Powered QA Testing
Test case generation from requirements. Real browser execution. Automated defect tickets with screenshots and traceability.
Voice Agents
Inbound and outbound phone and voice on GoOmni. High volume, brand voice, full transcripts and CRM sync. Intake, scheduling, phone screens, order calls, and more.
Knowledge Intelligence
Indexed knowledge, concepts, and ontology so retrieval stays consistent across agents. Documents, drives, and crawled sites in one searchable layer with source-linked answers.
Workflow Orchestration
Multi-step automation across Jira, Salesforce, HubSpot, Asana, Google Workspace, and custom APIs. One trigger, full execution chain.
Proposal & RFP Automation
Generate RFP responses grounded in your documents. Scan procurement portals. Route opportunities to the right team automatically.
All capabilities ship with governance controls, full audit trails, cost monitoring, and role-based access. Available as standalone deployments or integrated into your existing stack.
Questions we hear
before every project.
AI Agents Newsletter
One short, opinionated note per week.
Capability, maturity, and the four numbers your CFO will ask. No fluff, no recycled vendor pitches.
Compare 13 AI models. Understand agent architecture.
Same prompt, different models, side by side. See the speed, cost, and quality difference between proprietary and open-source AI. Then explore what makes an agent tick.
One company. Two starting points.
Pick the one that hurts more today.
Start at /agents
You have a workflow that burns hours and you want an agent in production. Get the four-number ROI before you write a check.
Scoping assessmentStart at /test
Your agent is in production and you do not yet have proof it stays correct under load. Bring the rigor that tested the systems running banks and federal ops.
Test approachEither door, four numbers and an honest grade.
No outside capital. No PE owner. No exit timeline. The owner-operator picks up the phone.
Senior consultants on every engagement. The principal who scopes it is the principal who runs it.
We brought the rigor that ships the world's hardest software to the agents now running it.
Start with an AI
Readiness Assessment.
Two to four weeks. Clear map of where AI will deliver the highest ROI.
More in this area
Articles, talks, guides, case studies, and reference artifacts that show up on the same kinds of engagements.
- Operating Model
The AI Agent Maturity Model
Five levels, seven capability axes, and the four numbers your CFO will ask. The honest picture of what it takes to run AI agents in production, and how most teams overrate themselves by 1.5 levels.
Read → - Whitepaper
Starting AI Adoption: A Sequence for Mid-Market Engineering Teams
The order of operations we use with mid-market engineering teams that have been told to ship AI and do not know where to start. Six stages, named exit criteria, the anti-patterns that predict failure, and the first-90-days view that ties architecture, evaluation, and model economics into a coherent adoption sequence.
Read → - Whitepaper
Evaluation Before Shipping: How to Test an AI Application Before It Hits Production
The release-gate playbook for AI features. Covers the five evaluation dimensions, how to build a lean golden set, where LLM-as-judge is trustworthy and where it lies, rollout mechanics with named exit criteria, and the regression suite that keeps a shipped AI feature from quietly rotting in production.
Read → - Whitepaper
Choosing the Right Model (and Knowing When to Switch)
A practical framework for matching LLM model tier to task. Covers the four axes (capability, latency, cost, reliability), cascade routing patterns that cut cost 60 to 80 percent without measurable quality loss, switching costs you did not plan for, and the worked economics at 10K, 100K, and 1M decisions per day.
Read → - Whitepaper
Workflow or Agent? A Decision Framework Before You Architect Anything
Most production 'agents' are workflows that overshot. This paper distinguishes deterministic LLM pipelines from autonomous agents, names the four questions that decide which one to build, and covers the failure modes specific to each path. Includes the 'earned autonomy' principle for promoting workflows to agents only after instrumentation justifies it.
Read → - Whitepaper
The Case for Investing in Testing: A Board-Level Argument for Enterprise Test-Function Capability
Enterprise organizations regularly face the question of whether to invest in their test-function capability, in hiring, in tooling, in automation infrastructure, in process maturity. The question is often answered by default rather than by analysis, and the default is under-investment relative to the economic case. This whitepaper presents the board-level argument for investing in testing, structured around the four business outcomes that robust testing produces, the cost curve that makes early investment asymmetrically valuable, and the specific organizational patterns that distinguish organizations that treat testing as strategic from those that treat it as overhead.
Read →
Where this leads
Services and products that typically come next.
- Service · AI
AI & Data Governance
Building AI systems that work in production: architecture, governance, and the failure-mode coverage prototypes hide.
Learn more → - Solution
Risk Reduction & Clear Decisions
Quality programs and decision frameworks that shift risk discussions from anecdote to evidence.
Learn more → - Tool · AI
Goomni
AI voice agent for inbound coverage: appointment scheduling, FAQ handling, intake. Deployed for our own line and for clients.
Learn more →