Services Console

Turn AI consulting intake into a scoped audit plan and pipeline decision.

Capture prospect details, generate a deterministic engagement scope, and track status without external AI calls or hidden scoring.

Start Intake

Advisory intake journey

Move from first signal to scoped engagement with commercial clarity.

Make the path from intake, risk posture, and package comparison feel guided without hiding the deterministic rules.

Guided intake
Commercial clarity
Delivery path

Scope engagement Compare packages Run full eval in AI Testability

Active Prospects

Saved in this browser

Default SLA

10d

Launch readiness sprint

Service Packages

Ready for deterministic scope

Feedback Queue

GitHub issues when env is set

Consulting Intake

These inputs are the only drivers of the generated scope. Changing any field updates the recommendation immediately.

Company or engagementUsed when saving this scope into the prospect pipeline.Company stageFormalize evals, owners, and customer-facing controls.AI use caseTimelineFast scope and launch readiness decision.Current stack

Risk areas

Select the concrete risks the engagement must investigate.

Deterministic Scope Generator

The package is chosen from a fixed scoring rule. No AI, API, or background call is used.

Recommended Package

Governance + Eval Buildout

Best for growth and enterprise teams building repeatable AI assurance operations.

Critical

Timeline

4-6 weeks

Commercial

Scoped retainer

Rule path

3 stage + 4 use case + 2 timeline + 3 risks

Deliverables

Intake summary with assumptions, constraints, and launch decision criteria.
AI system inventory covering data sources, model vendors, human review, and owners.
Prioritized risk register with severity, owner, mitigation, and proof needed.
Customer journey risk map with launch approval gates.
Golden-task evaluation pack with acceptance thresholds.
Data handling review covering retention, redaction, and vendor boundaries.
Measurement plan for offline evals, review sampling, and production monitors.
Engagement roadmap with weekly milestones and decision checkpoints.

Open Questions

Who owns executive sign-off for launch, remediation, or pilot expansion?
Which production data can be sampled for evaluation without creating new risk?
What customer, compliance, or security evidence must be produced before launch?
What internal milestone should this engagement unblock?
Which parts of the current stack are mandatory versus replaceable?
Which examples define a correct, borderline, and failed response?
Which data classes are prohibited from model prompts or logs?
What signals will prove the system is safe enough to launch?

Scope explanation

Series A / scaling + Customer-facing copilots + 3 selected risk areas. The console prioritizes higher-governance packages when stage, regulated risk, security exposure, or timeline pressure increase.

Risk controls

Confirm named business, technical, security, and review owners before work starts.
Define stop/go gates for pilot, launch, and rollback decisions.
Require golden tasks, reviewer sampling, drift thresholds, and launch-blocking eval gates.
Map prohibited data, retention limits, redaction ownership, and vendor logging boundaries.
Define offline eval coverage, production monitors, sample rates, and failure triage SLAs.

Prospect Pipeline

Track engagement status from intake through audit delivery and follow-up.

Current intake readiness: Customer-facing copilots is not launch-ready. Treat this as a control-build engagement before any expansion, with executive sign-off and evidence review before release.

5 saved prospects

New Intake1

Northstar Health

Growth · Decision support

Model Risk Audit

Status

Confirm PHI boundary and clinical owner

Scoping1

LedgerLoop

Series A · Agentic workflows

Launch Readiness Sprint

Status

Map tool permissions and approval policy

Proposal Sent1

CampusOps

Seed · Internal automation

Automation Feasibility Sprint

Status

Send fixed-fee acceptance deadline

Audit Active1

ScaleRiver

Enterprise · Customer-facing copilots

Governance + Eval Buildout

Status

Review eval rubric with security

Follow-up1

ClausePilot

Seed · Knowledge retrieval

Launch Readiness Sprint

Status

Schedule monitor implementation review

Pipeline Table

Prospect	Package	Next step	Value	Due
Northstar Health	Model Risk Audit	Confirm PHI boundary and clinical owner	$18k	Jul 2
LedgerLoop	Launch Readiness Sprint	Map tool permissions and approval policy	$12k	Jul 5
CampusOps	Automation Feasibility Sprint	Send fixed-fee acceptance deadline	$6k	Jul 8
ScaleRiver	Governance + Eval Buildout	Review eval rubric with security	$32k	Jul 12
ClausePilot	Launch Readiness Sprint	Schedule monitor implementation review	$12k	Jul 16

Service Packages

Fixed offers keep AGI Consultant practical: audit evidence, launch gates, and operating plans.

Automation Feasibility Sprint

Best for early internal workflows with narrow data access and clear human review.

5 business days$6k fixed

Feasibility scorecard
Workflow risk map
Pilot backlog

Launch Readiness Sprint

Best for teams preparing a customer-facing or revenue-critical AI launch.

10 business days$12k fixed

Launch gate checklist
Eval starter pack
Risk register

Model Risk Audit

Best for products with privacy, security, regulatory, or accuracy exposure.

3 weeks$18k fixed

Control review
Red-team plan
Executive findings deck

Governance + Eval Buildout

Best for growth and enterprise teams building repeatable AI assurance operations.

Current

4-6 weeksScoped retainer

Governance operating model
Eval roadmap
Monitoring design

Feedback Plumbing

The shared floating feedback widget remains wired to Firebase captures and GitHub Issues when environment variables are configured.

GitHub repo target: alibad/agiconsultant

Operating Rules

Keep recommendations deterministic and traceable to intake answers.
Position services around audit evidence, owners, and launch gates.
Avoid broad claims about predicting AGI timelines.

Open domain

Proposal Email

A client-ready first follow-up based on the current scope and risk posture.

Subject: Governance + Eval Buildout proposal for Northstar Support

Hi —

Based on the current Series A / scaling context, customer-facing copilots use case, and 2-week sprint timeline, I recommend starting with a Governance + Eval Buildout.

Customer-facing copilots is not launch-ready. Treat this as a control-build engagement before any expansion, with executive sign-off and evidence review before release.

Proposed scope (4-6 weeks, Scoped retainer):
- Intake summary with assumptions, constraints, and launch decision criteria.
- AI system inventory covering data sources, model vendors, human review, and owners.
- Prioritized risk register with severity, owner, mitigation, and proof needed.
- Customer journey risk map with launch approval gates.
- Golden-task evaluation pack with acceptance thresholds.

Controls to confirm before kickoff:
- Confirm named business, technical, security, and review owners before work starts.
- Define stop/go gates for pilot, launch, and rollback decisions.
- Require golden tasks, reviewer sampling, drift thresholds, and launch-blocking eval gates.
- Map prohibited data, retention limits, redaction ownership, and vendor logging boundaries.

Open questions for the discovery call:
- Who owns executive sign-off for launch, remediation, or pilot expansion?
- Which production data can be sampled for evaluation without creating new risk?
- What customer, compliance, or security evidence must be produced before launch?
- What internal milestone should this engagement unblock?

If this is useful, the next step is a 30-minute scoping call to confirm owners, available evidence, and the decision date.

Next Operator Action

Take the copied scope into a discovery call, verify assumptions, and update the prospect card once package fit and launch blockers are confirmed.