Capabilities
What we build, how it's done, and why it works.
Bespoke AI Development & Strategy
Purpose-built enterprise moats, proprietary workflows, and regulated domain-specific agent deployments.
Enterprise-Ready Agentic Products
Nearly 40 proprietary, production-tested agents & engines. Includes our flagship enterprise second brain, Morgan.
Training & Practice Environments
Private, hands-on working sessions with Groundwork engineers building live automations using your organization's data.
AREA 01
Bespoke AI Development & Strategy
Purpose-built systems that constitute a competitive moat, encode proprietary workflows, or run a regulated process. This is the right fit when the outcome is specific to the organization and cannot be solved with standardized tooling — when the system must reason over proprietary data, follow domain rules, or integrate with internal systems.
What We Build
Domain-Specific Agents
Multi-agent orchestration engineered for complex, multi-step enterprise business processes.
Custom RAG Architecture
High-precision RAG systems indexing proprietary corporate corpora with reciprocal rank fusion.
Internal App Integration
Native AI agents directly embedded into existing internal applications and enterprise databases.
Regulated Deployments
Compliance-grade agent deployments meeting strict audit, PII, and security requirements.
How We Engineer
Human-Directed Intelligence
Every agent runs under human-engineered constraints. Operating experience lives in context windows, prompts, and validation gates. The agent executes; the human directs.
Modular & Version-Controlled
Every workflow and prompt is versioned with documented change history. Fail-safe design ensures no single point of failure corrupts output.
Context Over Prompting
We prime agents with domain context, specify output schemas, embed quality gates catching errors before propagation, and direct chain-of-thought for auditability.
Cross-Source Validation
Every significant finding is corroborated by at least two independent sources. Conflicting data triggers investigation, never an averaged conclusion.
Production Stack Architecture
Models
- Anthropic Claude
- OpenAI
- GLM
- Kimi-k
- Specialized models
Orchestration
- Hermes
- pi
- Claude Code
- n8n
- Make.com
Development
- Cmux
- Cursor IDE
- Claude Desktop
- Docker
- Custom MCP servers
Data Infrastructure
- PostgreSQL with pgvector
- Indeed
- RepVue
- PRNewswire
- Financial databases
4-Phase Engagement Roadmap
Audit & Foundation
Map active agents and tools, inventory context pipelines, assess unit maturity, establish data governance.
Context Infrastructure
Deploy protocol layer with access controls, semantic indexing, freshness guarantees, security scope review.
Capability Mapping
Classify workflows, define human-in-the-loop patterns, designate AI workflow architect, standardize toolchain.
Intent Encoding
Translate OKRs into machine-readable goal structures, build delegation frameworks, deploy drift detection.
Quality Standards & Measurement
Delivery Standards
- <2%Factual correction requests across all client deliverables
- 100%Quantified claims traceable to verifiable source documents
- <10%Deliveries requiring substantive revision after initial review
- <5 hrsClient time commitment required per flagship engagement
Governance Benchmarks
AREA 02
Enterprise-Ready Agentic Products
A portfolio of over 40 proprietary, in-market agents and automation engines aiding growth and operations across numerous business functions.
The Groundwork Agent Suite
Enterprise Engines
Demand Intel ABM Engine, ConnectIQ Account Intelligence, SealSync Proposal Engine, Thought Leadership Engine, Onboarding Automation, and more.
AEO & Visibility
AEO/GEO Optimizer, Prompt Tracker, and Content Intelligence Agent.
Voice & Conversational AI
Murphy AI Voice Agent, Chloe Scheduling Assistant, and custom chatbots.
Sales Outreach & Intelligence
Automated outreach pipelines, lead qualification, and dynamic territory analysis.
Strategic Research Agents
Deep-dive sector analysis, competitor monitoring, and automated briefing documents.
The Enterprise Second Brain, "Morgan"
Morgan, named after co-founder Kerri Gaither's daughter, is an AI-powered knowledge system that continuously ingests, organizes, understands, and generates from an organization's employees, communications, documents, and decisions.
Morgan is not an open-box LLM (Claude, ChatGPT), a chatbot, and not document search — it is a governed, secure, policy-driven, closed-loop system.
Day-to-Day Production Workflow Outputs
Why Morgan Is a Different Category — A True Shared Brain
Individual AI tools like Claude, Copilot, and ChatGPT reset when the individual's session closes, with no persistent governance, compliance, or security layer for sensitive information, internal or external.
Morgan is a centralized, persistent, structured intelligence layer that every team member queries from the same source — the difference between each employee having a smart tool and the organization having a shared brain.
All data flows into one governed database, so collective knowledge is queryable by anyone with appropriate access: a new team member can get up to speed on a client without hunting through six Slack channels and twelve Zoom recordings.
Quality and Continuous Learning
Morgan runs on a closed loop of sensors, policy, tools, quality gates, and learning. Every generated document passes LLM-as-judge grounding checks, with unsupported claims flagged for human review. Fifty-one golden questions run weekly against the knowledge base (current pass rate: 96.1%), with regressions triggering alerts. 988 automated tests cover connectors, search, RAG, generation, access control, and PII scanning — currently passing with zero failures. Search misses are tracked, feedback is captured in structured form, and prompt versioning enables A/B comparison against historical performance.
Unified Ingestion
Scheduled connectors across Gmail, Slack, Zoom, Google Drive, Notion, and knowledge vaults normalize into a single searchable knowledge base.
Retrieval Independent of Phrasing
Semantic search, full-text search, and fuzzy matching are merged through Reciprocal Rank Fusion, so the strongest result from each method surfaces regardless of how the question is worded.
Answers with Provenance
Every answer is grounded in organizational data, carries source citations, and is tagged with the policy version and model used. A Mixture-of-Agents router selects lightweight models for simple queries and frontier models for complex synthesis.
Document Generation, Not Template Filling
Morgan retrieves relevant context and synthesizes new prose against approved templates: SOWs, MSAs, counsel-reviewed service agreements, SOPs, process documents, GTM briefs, and proposals. Gaps are marked [REQUIRES HUMAN INPUT] rather than filled with plausible fiction. Approved drafts export as branded .docx.
Governance as Architecture
Governance is engineered into Morgan's infrastructure, not left to individual employee behavior:
Classification at Ingestion
PII detection (SSNs, credit cards with Luhn validation, routing numbers, API keys, IBANs) automatically marks records RESTRICTED and removes them from all search paths before they are ever queryable.
Three-Tier Access Control
BUSINESS, PERSONNEL, and EXECUTIVE tiers enforced at both API and database level; PostgreSQL Row-Level Security means a misconfigured API still cannot return restricted rows, and no application role can DELETE.
Data Residency
Self-hosted deployment, secrets in an encrypted vault with keys never mounted in the application container, backups encrypted before offsite transfer, TLS and Bearer authentication on all endpoints.
Approval Workflow
DRAFT → IN_REVIEW → APPROVED/REJECTED with Slack notifications; only approved documents export. Morgan prepares drafts only — it is not authorized to publish, email, contract, purchase, or alter live systems.
Policy-As-Code
System rules and brand policy live in a versioned database table assembled at runtime; governance is enforced on every query, not filed in a folder.
Prompt Injection Defense
All ingested content is treated as untrusted data; instructions embedded in emails, transcripts, or web pages are never executed as commands.
Full Audit Trail
Ingestion, query, response, generation, and feedback logs including retrieved chunk IDs, similarity scores, latency, and policy version.
AREA 03
Training & Practice Environments
Practice Environments are private, hands-on working sessions in which Groundwork engineers sit with an internal team and build fully functional automations using that team's own data, systems, and domain requirements. Participants do not watch a demo; they leave with automations running in their own environment and the means to extend them.
Session Formats
Half-Day Session
One functional automation built end to end against a real workflow, plus the reasoning behind each design decision.
Full-Day Session
Multiple connected automations, an initial workflow taxonomy, and a working pattern the team can replicate.
Multi-Day Program
A portfolio of automations across functions, with internal owners identified and equipped to maintain them.
How a Session Runs
01. Scope & Prep
The team brings real workflows, not hypotheticals — we scope which are agent-ready before the session begins.
02. Live Build
We build alongside the team in their environment, against their data and constraints.
03. Design Narration
Every decision is narrated: why this model, why this validation gate, why this step stays human-in-the-loop.
04. Working Delivery
Automations ship working, with gaps and follow-on work documented against named owners.