Build agents that
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Production-grade AI systems that handle real workloads with monitoring, governance, and human-in-the-loop controls built in from day one.
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.
Assessment first.
Always.
Every engagement starts with a 2-4 week AI Readiness Assessment. We map your systems, identify the highest-ROI targets, and architect a production path before writing a single line of code.
Assess
2-4 weeksEvaluate current state. Map data, systems, and workflows. Identify highest-ROI automation targets.
Architect
Production-grade infrastructure design. Security model, governance framework, integration plan.
Build
8-12 weeksWorking AI systems with monitoring, fallbacks, and human-in-the-loop controls. Not prototypes.
Scale
Measure outcomes, tune performance, expand to new workflows. Continuous optimization.
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.
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.
- 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 → - Whitepaper
Deciding When to Bring in External Help: A Framework for Training, Consulting, Staff Augmentation, and Outsourced Testing
Most enterprise decisions to bring in external testing help succeed or fail based on whether the right form of help was selected, not on whether the particular vendor performed well. This whitepaper covers the four categories of external testing help — training, consulting, staff augmentation, and outsourced testing — and the decision framework that matches each form to the problem it solves, with cost, capability, and exit-cost implications for modern enterprise test programs.
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
Allora
Lead intelligence agent that verifies every claim before it reaches your CRM. Production AI we run ourselves.
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 →