Enterprise Context Engineering

Turn broken workflows into working AI systems.

Start with one workflow. We connect the systems behind it, structure the context, and ship AI output your team can actually use.

Get a systems architecture review See example workflows

One workflow first → working system in 4–6 weeks → expand systematically.

Built for B2B ops, sales, and support teams with scattered data and repeatable workflows. 20+ years engineering leadership · 100+ products shipped · 5.0 Clutch

You don't need an AI platform first.
You need one workflow that works.

Pick the workflow your team repeats every week, and hates every time. We'll connect the systems behind it and make it work.

Deal brief generation

Sales

Turn scattered account context into a one-click prep packet for sales.

Before After
  • Rep searches CRM, calls, docs
  • 30–45 minutes manual prep
  • Inconsistent follow-up quality
  • ✓ One-click prep packet
  • ✓ Account context + next steps
  • ✓ Draft follow-up in seconds
30+ min → <30s
Click to toggle

Proposal generation

Ops

Turn calls, CRM, and approved templates into a draft proposal.

Before After
  • Relisten to calls
  • Copy into templates
  • Inconsistent scope
  • ✓ Structured proposal draft
  • ✓ Based on calls + CRM + templates
  • ✓ Consistent every time
2–3 hrs → <2 min
Click to toggle
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Renewal risk summary

Support

Surface account risk early from tickets, calls, CRM, and usage signals.

Before After
  • Signals buried across tools
  • Risk found too late
  • No unified view
  • ✓ Unified risk summary
  • ✓ Real-time recommended actions
  • ✓ Proactive outreach
Reactive → Real-time
Click to toggle

Why most AI efforts stall after the demo

The model is rarely the problem. The workflow around it is.

Disconnected data

AI gets fragments from 5+ tools instead of connected context.

Generic outputs

No business meaning, no relationships, no structure behind the prompt.

Inconsistent results

Same question, different answer. Every time.

No feedback loop

No evals, no quality tracking, no way to improve over time.

Outputs stop at a draft

AI writes something, but nothing connects to CRM, email, or workflows.

No production path

The prototype worked. Deploying it to the team never happened.

What actually makes AI useful

Fix the workflow, not just the prompt.

What most companies try

  • Better prompts
  • More tools
  • Bigger models
  • More training

What actually changes outcomes

  • Connected systems
  • Structured business context
  • Workflow integration
  • Evaluation and feedback

The system behind a working workflow

Three systems working together. Most companies only build the first.

Context

What AI can see

  • Connected data across your apps
  • Role-based access controls
  • Business objects and relationships
  • Retrieval tuned per workflow

Intelligence

What AI can do

  • Agents and multi-step workflows
  • Human review checkpoints
  • Actions into CRM, email, docs
  • Retrieval + reasoning strategies

Control

How AI gets better

  • Testing and eval cases
  • Feedback loops
  • Cost and usage visibility
  • Security and compliance

How systems connect to outputs

Hover any source or output to trace the connection.

Your systems

CRMSalesforce
DocsGoogle Docs
CallsGong
TicketsZendesk
EmailGmail
ChatSlack
ContextConnectors · Permissions · Business objects · Retrieval
IntelligenceAgents · Workflows · Multi-step · Human review
ControlEvals · Feedback · Tracing · Cost · Security

What your team produces

Deal brief<30s
Account context + next steps
Proposal draft<2 min
Calls + CRM + templates
Follow-up<30s
Discovery summary + action items
Risk summarylive
Tickets + usage + signals
Reporton-demand
Structured from multiple sources
Routingreal-time
Classification + assignment

Hover to explore how systems connect to outputs

40+ connectors — the tools your team already uses

Sales
Salesforce HubSpot Gong Fireflies
Messaging
Slack Microsoft Teams Gmail Discord
Ticketing
Jira Zendesk Linear Freshdesk
Knowledge Base
Confluence SharePoint Notion Guru
Cloud Storage
Google Drive Dropbox AWS S3 Egnyte
Code
GitHub GitLab Bitbucket
See what's blocking your AI rollout
Case study

From 30-minute prep to real-time follow-up

Before
  • 5 disconnected tools
  • 30+ min manual prep
  • No consistency across reps
After
  • Real-time discovery summary
  • Draft follow-up in seconds
  • Full pipeline visibility
Client B2B sales org, 40+ reps, enterprise pipeline
Problem Call transcripts, CRM data, email threads, and docs scattered across 5 tools. Reps spent 30+ minutes prepping each follow-up. No consistency across the team.
Systems Salesforce, Gong, Gmail, Google Drive, HubSpot
What we built Context layer across all sources + retrieval logic + automated discovery summaries, follow-up drafts, and pipeline classification.
Timeline Roadmap to working system in 5 weeks.
85%+ Classification accuracy
<30s Time to summary
3× Follow-up consistency
Real-time Pipeline visibility

"MetaCTO stood out for their ability to quickly grasp the intricacies of our product and translate that into clean, scalable solutions."

Bo Abrams, CEO, ATP
Read the full case study →

Systems don't stop at launch. They improve.

After the first workflow ships, we measure outputs, fix weak points, and expand what works into more workflows.

Observe Usage, outputs, feedback, cost
→
Evaluate Accuracy, failures, business alignment
→
Improve Prompts, data, workflow logic, retrieval
→
Expand New workflows, more users, more value
Workflow 1 → Workflow 2 → Workflow 3 → Org-wide system

We don't just ship AI. We make it better every week.

How to get started

Three steps from first call to working system.

01 Free

Discovery call

We review your systems, workflows, and where context is breaking.

You leave with: fit, feasibility, and the likely first use case.
02 Free

Opportunity map

We define the best first workflow, map systems gaps, and outline ROI potential.

You get: scoped roadmap, architecture, and workflow outline.
03 Paid

First working system

We connect your systems, build the context layer, and ship one production workflow.

You get: working output in 4–6 weeks.

Is this right for you?

Good fit

  • B2B team with CRM, docs, tickets, and calls
  • AI outputs are too generic or unreliable
  • You need AI in workflows, not in a tab
  • Budget and internal owner for AI

Not a fit

  • Marketing chatbot or consumer product
  • No data inventory or systems audit
  • No internal engineering or ops owner
  • Looking for a SaaS tool, not a system
Systems-firstWe build context infrastructure, not prompt wrappers
Production focusWorking output in 4–6 weeks, not a 6-month roadmap
Operator-ledRun by CTOs who have built and shipped 100+ products
Tool-stack neutralNo vendor lock-in. We connect what you already use.

Get a systems architecture review

20 minutes with a CTO. We'll look at your systems and tell you what's blocking production AI. No pitch deck.

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