SERVICE_CATALOG

Brainforge AI Service Catalog

Purpose: Complete catalog of all services with pricing and scope
Use: Service selection, scoping, positioning
Last Updated: 2026-02-23
Pricing from discovery: For low-tier limits ($1K–$3K by category), expected range, effort to execute, and how to map discovery call scope to catalog and price, see PRICING_AUDIT_OFFERS_AND_MATRIX_2026-02.md (§1.6, §1.7) and DYNAMIC_PRICING_GUIDE.md.

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📑 Table of Contents

• Service Overview
• Entry Services
• Core Services
• Premium Services
• Retainer Packages
• Training & Enablement
• Service Comparison

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🎯 Service Overview

By Price Point

Price Range	Service Type	Typical Duration	Best For
$5,000-10,000	Entry	1-3 weeks	Quick wins, assessments
$10,000-30,000	Core	3-8 weeks	Infrastructure projects
$30,000-50,000	Advanced	8-12 weeks	Platform builds
$50,000+	Premium	12+ weeks	Custom platforms
$10,000-40,000/mo	Retainer	Ongoing	Continuous support

By Service Category

Category	Services	Starting Price
Assessment & Audit	Data Audit (Marketing, Product, dbt), Tool Assessment	$5,000-10,000 per function
Implementation	Tool/Stack (ETL, Governance, BI, Analysis), ETL, Warehouse	$5,000-40,000
Full Platform	Discovery → Phased Build (data map, dictionary, architecture)	$15,000+ discovery
AI & Context	AI Copilot, Custom Context Graphs, Knowledge Base	$7,500+
Platform Build	Analytics Platform, Custom Apps	$25,000-200,000+
Ongoing Support	Retainers, Maintenance	$10,000/month
Training & Enablement	Workshops, training programs, enablement retainers	$3,000-15,000/$5,000/mo
Advisory	Strategy, Architecture	$250/hour

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🧩 Service Design Principles

Our services are not ad-hoc deliverables. They are designed products with consistent shape, guardrails, and proof of value. This section codifies the principles that govern how we construct, package, and scale services. Any new or revised service must be able to pass these checks before it’s added to the catalog.

When evaluating or designing a service, we apply four checks:

1. Immediately Actionable > Analytically Sophisticated

   • Clients value interventions they can act on today over long-cycle insights.
   • We optimize for services that show impact in < 8 weeks.

2. Workflow-Integrated > Standalone

   • Services must embed into existing client tools (Slack, Teams, Salesforce, EMR, etc.), not require separate “analysis phases” that live off to the side.
   • If adoption requires heavy process redesign just to get started, the service fails this test.

3. Problem-Specific > General Purpose

   • Each service must solve a clear, high-stakes operator pain point (e.g., reduce CAC, triage patients, close revenue leakage, de-risk trials).
   • “Analytics for everything” or purely exploratory “data work” is not our model.

4. Fast Feedback Loops > Comprehensive Data Collection

   • We favor services that generate measurable results in weeks, not quarters.
   • Iteration speed is a competitive advantage; perfection is not. If a service can’t support tight learn–build–measure loops, we redesign it.

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💼 Entry Services ($5,000-$10,000)

1. Data Audit & Tracking Validation (Function-Specific)

Price: $5,000–$10,000 per function (1–3 weeks, depending on scope)

Audits are function-specific: we assess data, tracking, and tooling for one business function at a time so deliverables are focused and actionable. Full per-function audit typically lands in the $5–10K range.

Variants (choose the function that matches the need):

Function	What we audit	Typical deliverables	Reference
Marketing Data Audit	Ad platforms (Meta, Google, etc.), Google Tag Manager setup, tracking and attribution accuracy. Whether you can see true cost and revenue by channel, ROAS/CAC, and where gaps (e.g. no LTV, no funnel, GA4 shelved) limit optimization.	Channel/spend review, ROAS/CAC view, attribution gaps, prioritized recommendations memo; optional 1-pager on team/vendor effectiveness. Lite option: ~$1,500 for “second set of eyes” only.	Digital Ads Visibility Audit, Ilmor SOW
Product Analytics Data Audit	Tracking plans, event taxonomy, and whether the right events are in place so clients can understand what users do in the product. Tool coverage (Segment, GA4, Amplitude, etc.), violations, and missing data.	Current-state data model, master tracking plan (events/properties), violation checklist, recommendations deck, 2-hour stakeholder presentation.	SOWs: Trek, Honey Stinger
dbt Audit	dbt codebase structure, documentation, modularity, testing, and run performance. Whether the project is scalable and ready for new hires; where repeated code, long run cycles, or knowledge loss create risk.	Audit report, roadmap toward efficiency and confident data, faster new-hire ramp. 3–4 weeks.	dbt Onboarding Accelerator — $10,000 for audit + roadmap

What You Get (common across functions):

• ✅ Current-state documentation and gap analysis for that function
• ✅ Prioritized recommendations (quick wins vs. longer-term fixes)
• ✅ Written deliverable (memo, deck, or report) and review call
• ✅ 30-day email Q&A where applicable

What’s Not Included:

• ❌ Implementation of fixes (separate engagement)
• ❌ Code changes or tool migrations
• ❌ Auditing a different function (scope separately)

Timeline: 1–2 weeks (Marketing Lite/Standard, Product Analytics); 3–4 weeks (dbt Audit).

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2. Data Tool Implementation & Full Data Stack

Price: $5,000+ per layer or bundle (2–6 weeks, varies by scope)

We take a full data stack view: implementations can cover a single layer or the full pipeline. The stack is framed as:

Full data stack (feedback loop)
ETL Data Foundations → Governance Layer → Business Intelligence Layer → Analysis Layer

• ETL Data Foundations — Ingestion from source systems (CRM, EHR, ad platforms, e‑commerce, docs, tickets, code). Connectors, backfill, incremental loads.
• Governance Layer — Definitions, lineage, ownership (RACI), data dictionary, naming conventions, change control. “Documentation first” so build doesn’t start until definitions are signed.
• Business Intelligence Layer — Reporting and dashboards (Looker, Tableau, Mode, Metabase, etc.), unified metrics, self-serve access.
• Analysis Layer — Product analytics, experimentation, cohort/funnel analysis, and activation (reverse ETL, CDP, etc.).

Within the Analysis Layer, we anchor work in two archetypes. Every analytics-focused service line should map clearly to one of these:

1. Customer Experience Optimization (Marketing Needs Speed, Not Deep Analysis)

   • Audience shift: Product teams can afford slow, weeks-long A/B tests with small samples. Marketing teams can’t—they run campaigns across tens of thousands of users and need answers tomorrow, not next quarter.
   • Principle: Shrink the gap between “what the data shows” and “what you should do.” Analytics should be embedded directly into campaign workflows (ads, email, landing pages), not sit in dashboards waiting for interpretation.
   • After state: Experimentation runs at operational speed—launch in the morning, see performance by afternoon, and reallocate budget automatically across channels.
   • Hypothetical pattern: Marketing automation + analytics + AI: system launches 10 email or ad variants, detects winners in real time, shifts spend, and generates the next optimized copy without analyst intervention.
   • Target users & example services: Marketing teams, CS ops, clinic ops. Example service lines: AI Intake Optimizer, Knowledge RAG Hub, Experimentation Accelerator.

2. Revenue Intelligence (From Product Metrics to Operator Decisions)

   • Audience shift: Traditional analytics reports “what users did” to product managers. CFOs and COOs care about churn risk, CAC efficiency, and margin pressure, not feature clicks.
   • Principle: Analytics must connect behavioral data directly to financial levers (revenue, margin, retention). Every insight should map to a concrete operator decision.
   • After state: Data doesn’t just describe risk—it prescribes action. Example: “This cohort has 20% churn risk → trigger save campaign,” or “Upsell experiment across 10k accounts → projected $3M ARR lift.”
   • Hypothetical pattern: Analytics + CMS + AI closes the loop: real-time data identifies a visitor persona, AI generates optimized messaging, the CMS auto-adapts the experience, and analytics attributes revenue impact in weeks, not quarters.
   • Target users & example services: CFO, COO, revenue leaders. Example service lines: Revenue Intelligence Dashboards, Customer 360, Product Analytics Accelerator.

What we can do:

• Single-layer implementation — e.g. one analytics tool (Segment, GA4, Amplitude), one BI tool, or ETL for a set of sources. Typical 2–3 weeks, $5K–15K depending on complexity.
• Multi-layer or full-stack — Channel-by-channel or source-by-source; explore → define & map → ingest & backfill → deliver value. See Full Data Platform Assessment & Implementation for discovery-through-build engagements.

Perfect For:

• Companies adopting a new analytics or BI tool
• Migrating from one tool to another
• Adding first data warehouse and transformation layer
• Implementing product analytics or reporting for the first time

What You Get:

• ✅ Tool selection guidance (if undecided)
• ✅ Account setup, configuration, and integration with existing systems
• ✅ Basic event instrumentation and/or pipeline setup per layer
• ✅ Team training and documentation

Add-ons: Additional tools/layers +$2,000–5,000each;customdashboards+$2,000–3,000; 3-month support +$2,000/month.

Reference: LMNT discovery example (documentation-first, channel-by-channel, stage-gated).

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3. AI Copilot Integration Sprint & Custom Context Graphs

Price: $7,500+ (1–2 weeks for pilot; custom context graph builds vary by scope)

What It Is: We help teams move past generic LLMs (ChatGPT, Claude) to indexed knowledge bases and custom context graphs so AI is grounded in your company’s data and how work actually gets done. Unlike solutions that stop at a chat interface, we push Slack/email automations, connections to BI reporting, and internal tooling so teams turn an AI-enhanced knowledge base into impactful decisions, not just Q&A.

How we build custom context graphs (inspired by Glean’s context graph approach):

• Deep connectors & observability — Ingest from the apps where work happens: docs, tickets, chat, code, CRM. Capture events (views, edits, comments, PRs, deploys, messages) and normalize them as traces.
• Knowledge graph — Unify entities (people, customers, projects, products) and reconcile identities across systems so activity is meaningful.
• Personal graphs — Per-user activity streams and task clustering (e.g. “investigate_alert”, “draft_spec”) with privacy boundaries.
• Abstracted traces — Anonymized, coarse-labeled process steps (action type, tool family, process tags) so we see “what tends to happen, in what order” without raw content.
• Context graph — Probabilistic model of how work flows; the playbook agents use. We form patterns from many users and traces, then learn from agent execution: successful paths reinforce good patterns; failures highlight anti-patterns.
• Beyond chat — We wire the graph into Slack/email automations, BI reporting, and internal tools so the knowledge base drives decisions and workflows, not only a chat window.

Perfect For:

• Agencies and consultancies (campaign brief generation, client intelligence, reporting automation) — see Building an AI-Native Agency use cases: 80% time reduction on briefs, 15 hrs/week recovered on reporting, meeting prep in minutes.
• Data and product teams adopting AI-assisted development (Cursor, Copilot, custom copilots).
• Companies that want one source of truth (transcripts, SOWs, playbooks) queryable by AI and actionable in daily tools.

What You Get:

• ✅ Indexed knowledge base (searchable, versioned, collaborative; markdown + version control).
• ✅ Pilot implementation (e.g. one use case: brief gen, reporting, or client intelligence).
• ✅ Architecture for context graph (connectors → knowledge/personal graphs → abstracted traces → context graph → agent feedback).
• ✅ Team enablement and best practices; optional Slack/BI/automation wiring.

What’s Not Included: Custom model training; unbounded enterprise platform build; ongoing support (add-on).

Add-ons: Additional use cases +$2,000–5,000each;extendedsupport+$2,000/month.

Reference: Building an AI-Native Agency (indexed knowledge base, campaign briefs, reporting, client intelligence); Glean: How do you build a context graph?.

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🏢 Core Services ($10,000-$50,000)

4. Full Data Platform Assessment & Implementation

Price: Discovery $15,000–$25,000 (3–6 weeks); full build phased at $40,000–$150,000+ over 3–12 months depending on scope.

What It Is: End-to-end from the ground up for organizations that need a centralized data platform with clear architecture, governance, and phased rollout—e.g. specialty health clinics, multi-system environments (EHR + CRM + research + operations), or multi-LOB companies. We start with discovery (data map, data dictionary, flow diagrams) so stakeholders see current state and agree on what “good” looks like; then we design the right architecture in phases so the foundation supports the next 2+ years and can be extended without teardown.

How we pitched it (specialty health clinic example):

• Discovery: Data map, data dictionary, and diagrams showing flow of data across systems (e.g. Health Cloud, ECW, Castor, Vault, Dropbox). Work with the team to validate: are we missing a data point? Is this the full picture of what’s needed to deliver care today?
• Historical load: Plan to get all historical data (including CSVs, exports) into a central repository in a consistent format—then execute in phases.
• Architecture: Right design for multi-LOB, future network of clinics, and regulatory needs. Phased build so they can accomplish the next two years without a full rebuild later. Cloud data warehouse (e.g. Azure for HIPAA; Snowflake/BigQuery where compliant) as the single place we control; connectors and orchestration (e.g. dbt) with version control and lineage.
• Context management: Metrics and definitions organized by topics (e.g. intake) so different departments see the milestones that matter to them; AI-queryable so non-technical users can ask questions and technical users can use SQL. Enterprise-ready: dbt, CI/CD, tests, documentation.
• Ongoing: Optional agentic AI, clinical decision support, or multimodal data (e.g. wearables, video) scoped as later phases once the foundation is in place.

Perfect For:

• Specialty health clinics (research + practice, EHR + CRM + trials)
• Multi-system orgs with no single source of truth
• Venture-backed or scaling companies that need a durable, adaptable data foundation before adding BI, AI, or new lines of business

What You Get:

• ✅ Data map, data dictionary, flow diagrams (discovery)
• ✅ Future-state architecture and phased implementation roadmap
• ✅ Centralized repository (cloud warehouse), ingestion, transformation (dbt), and governance (definitions, RACI, lineage)
• ✅ Optional: HIPAA-aligned environment, US-based or nearshore staff when required

Engagement structure: Fixed-price discovery is often possible when the profile matches prior engagements; then phased fixed-price or T&M for build. Can work alongside another vendor’s discovery output (e.g. reuse data map) and then take the lead on build.

Reference: Robert × Jake (Sunstone Therapies / Small Gray) discovery call; LMNT documentation-first plan; tier3-discovery-foundation template.

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5. ETL Platform Assessment & Implementation

Price: $15,000-$30,000 (3-4 weeks)

What It Is: Comprehensive assessment of your data sources, ETL tool recommendation, architecture design, and implementation roadmap. Choose the right ETL solution (Fivetran, Polytomic, custom) for your needs.

Perfect For:

• Companies scaling data infrastructure
• Migrating to modern data stack
• Consolidating data from multiple sources
• Pre-warehouse setup (needs ETL first)

What You Get:

• ✅ Current data sources audit (up to 10 sources)
• ✅ ETL tool recommendation with cost analysis
• ✅ Architecture design document
• ✅ Implementation roadmap (phased approach)
• ✅ Vendor selection support
• ✅ Cost-benefit analysis (build vs buy)
• ✅ Proof of concept for 2-3 sources
• ✅ Team training

What’s Not Included:

• ❌ Full ETL implementation (separate or add-on)
• ❌ Data warehouse setup (separate service)
• ❌ Ongoing pipeline maintenance (retainer)

Pricing Factors:

• Number of data sources: +$2,000 per complex source beyond 10
• Custom transformations: +$3,000-5,000
• Implementation included: +$10,000-20,000
• Ongoing support: +$3,000/month

Timeline: 3-4 weeks (assessment), 6-8 weeks (with implementation)

Team:

• Managing Data Lead: 16 hours
• Senior Data Engineer: 40 hours
• Technical Project Manager: 16 hours
• Data Analyst: 10 hours

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6. Data Warehouse Setup & Modeling

Price: $20,000-$40,000 (4-6 weeks)

What It Is: Full data warehouse setup (Snowflake, BigQuery, Redshift), dimensional modeling, dbt transformation layer, and data quality framework.

Perfect For:

• Companies building modern data stack
• Migrating from legacy warehouse
• Centralizing data from multiple sources
• Need for single source of truth

What You Get:

• ✅ Warehouse selection and setup (if needed)
• ✅ Dimensional data modeling (star/snowflake schema)
• ✅ dbt transformation layer setup
• ✅ Data quality checks and monitoring
• ✅ Documentation (models, lineage, definitions)
• ✅ CI/CD pipeline for transformations
• ✅ Team training (dbt, SQL, modeling)
• ✅ 30-day post-launch support

What’s Not Included:

• ❌ ETL setup (separate service, but can bundle)
• ❌ BI tool setup (add-on)
• ❌ Reverse ETL (add-on)
• ❌ Advanced monitoring (add-on)

Add-ons:

• Reverse ETL implementation: +$5,000
• Advanced BI setup (Looker, Mode): +$5,000-10,000
• Team training (advanced): +$2,000
• 3-month support: +$3,000/month

Pricing Factors:

• Number of data sources: Base includes 5, +$2,000 per additional
• Model complexity: Simple (1.0x), Moderate (1.2x), Complex (1.5x)
• Historical data migration: +$3,000-10,000

Timeline: 4-6 weeks

Team:

• Managing Data Lead: 20 hours
• Senior Data Engineer: 80 hours
• Technical Project Manager: 20 hours

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7. Product Analytics Platform Build

Price: $25,000-$50,000 (6-8 weeks)

What It Is: Complete product analytics platform from event taxonomy to dashboards. Enable your product team to make data-driven decisions with confidence.

Perfect For:

• Product teams needing robust analytics
• SaaS companies launching data-driven culture
• Companies outgrowing basic analytics
• Teams running experiments and needing infrastructure

What You Get:

• ✅ Event taxonomy design (events, properties, user traits)
• ✅ Tracking implementation (Segment, RudderStack, or direct)
• ✅ Analytics dashboards (10-15 key dashboards)
• ✅ Experimentation framework (A/B test infrastructure)
• ✅ Cohort analysis setup
• ✅ Funnel and retention analysis
• ✅ Product metric definitions
• ✅ Team training (analysts + PMs)
• ✅ 60-day support

What’s Not Included:

• ❌ Marketing analytics (separate focus)
• ❌ Custom ML models (add-on)
• ❌ Real-time alerting (add-on)

Add-ons:

• Marketing analytics dashboards: +$5,000
• Custom ML/predictive models: +$10,000-20,000
• Real-time alerting: +$3,000
• Extended support: +$3,000/month

Pricing Factors:

• Number of event types: Base includes 50, +$500 per additional 10
• Dashboard complexity: Basic (1.0x), Advanced (1.3x)
• Experimentation needs: Basic (1.0x), Advanced (1.4x)

Timeline: 6-8 weeks

Team:

• Senior Product Analyst: 80 hours
• Senior Data Engineer: 40 hours
• Managing Data Lead: 12 hours
• Technical Project Manager: 16 hours

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🚀 Premium Services ($50,000+)

8. Tech-Enabled Platform Build

Price: $75,000-$200,000+ (8-16 weeks)

What It Is: Custom platform development for agencies, enterprises, or product teams. Full-stack development with AI integration, data pipelines, and deployment infrastructure.

Perfect For:

• Agencies building internal tools
• Companies needing custom analytics platforms
• Tech-enabled service businesses
• Enterprises with unique requirements

What You Get:

• ✅ Platform architecture design
• ✅ Full-stack development (frontend + backend)
• ✅ AI integration (copilots, automation)
• ✅ Data pipeline setup
• ✅ Authentication & user management
• ✅ Deployment & hosting setup
• ✅ Documentation (code, API, user guides)
• ✅ Team training
• ✅ 90-day support

What’s Not Included:

• ❌ Ongoing hosting costs (client responsibility)
• ❌ Long-term maintenance (retainer)
• ❌ Feature development post-launch (retainer or new project)

Example Projects:

• Agency client dashboard with forecasting
• Custom analytics platform for e-commerce
• Internal tools for data teams
• AI-powered reporting systems

Pricing Factors:

• Platform complexity: MVP (1.0x), Full v1 (1.5x), Enterprise (2.0x)
• AI integration depth: Basic (1.0x), Advanced (1.5x)
• Integrations: Base includes 3, +$5,000 per additional

Timeline: 8-16 weeks

Team (Example for $100K project):

• Managing Data Lead: 60 hours
• Senior Data Engineer: 300 hours
• Senior Product Analyst: 80 hours
• Technical Project Manager: 60 hours

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🔄 Retainer Packages

9. Full-Service Data Management

Price: $10,000/month minimum (ongoing)

What It Is: Ongoing data modeling, pipeline maintenance, BI tool management, and data quality monitoring. Your extended data team.

Perfect For:

• Companies needing ongoing data support
• Teams without full-time data engineers
• Growing companies scaling data operations
• Businesses with seasonal data needs

What’s Included:

• ✅ Ongoing data modeling
• ✅ Pipeline maintenance and monitoring
• ✅ BI tool management
• ✅ Data quality monitoring
• ✅ CRM integration support
• ✅ Monthly reporting and reviews
• ✅ Ad-hoc analysis (within hours)
• ✅ Team support (Slack/email)

Volume Tiers:

Basic ($10,000/month): 50-60 hours/month

• Senior Data Engineer: 30 hours
• Data Analyst: 20 hours
• PM: 6 hours

Standard ($20,000/month): 100-120 hours/month

• Senior Data Engineer: 50 hours
• Data Analyst: 40 hours
• Managing Lead: 8 hours
• PM: 12 hours

Premium ($30,000/month): 150-180 hours/month

• Senior Data Engineer: 80 hours
• Data Analyst: 60 hours
• Managing Lead: 12 hours
• PM: 18 hours

Commitment Discounts:

• Month-to-month: Full price
• 3 months: -5%
• 6 months: -10%
• 12 months: -15%

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10. Dedicated Resource Retainers

Price: $16,000-$40,000/month

What It Is: Dedicated senior resource(s) embedded with your team. Think of them as your extended team member who knows your business deeply.

Perfect For:

• Companies hiring but need help now (3-6 month bridge)
• Seasonal projects with defined timeline
• Specialized expertise needed (fractional)
• Teams needing consistent, ongoing support

Resource Options:

Part-Time Senior Engineer ($16,000/month)

• 20 hours/week (half-time)
• 80 hours/month
• Rate: $200/hour

Full-Time Senior Engineer ($32,000/month)

• 40 hours/week (full-time)
• 160 hours/month
• Rate: $200/hour

Managing Data Lead (Advisor) ($10,000/month)

• 10 hours/week (advisory)
• 40 hours/month
• Rate: $250/hour

Mixed Team ($25,000-40,000/month)

• Custom mix of roles
• Senior Engineer + Analyst + Lead
• Flexible allocation

Commitment Discounts:

• Same as Full-Service retainer tiers

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📚 Training & Enablement

Training and enablement are a distinct service line: we help clients adopt tools, build data literacy, and run AI-augmented workflows through workshops, structured training programs, and ongoing enablement. We use a free 90-minute workshop (e.g. Cursor / knowledge-base) as a lead magnet for agencies and consultancies; paid offerings range from single-day workshops to multi-week programs and monthly enablement retainers.

Price: $3,000–$15,000 (workshops and programs); $5,000/month (ongoing enablement retainer).

Workshop offerings (1-day or half-day)

Format	What we cover	Typical price	When we use it
Cursor / AI-Native Agency Workshop	Hands-on setup: structure, index, and query your company’s intelligence using the same tools we use. Agency workflow templates, best practices from 50+ companies. Often run as free 90-min lead-gen for agencies; paid version can be half- or full-day with deeper customization.	Free (90 min) or $3,000–$5,000 (half/full day)	Agencies, consultancies; campaign driver (e.g. “free workshop” landing page).
Data tool / stack workshop	Best practices on a specific tool or layer (dbt, Segment, Looker, Snowflake, etc.). “How we run it” so your team can self-serve.	$3,000–$5,000	Post-implementation handoff; client asks for “training on the stack.”
Innovation day / AI tooling workshop	What’s out there today, best practices, less salesy—often with a partner (e.g. Snowflake, MoEngage). Educational, 20–30 people (closed-door dinners, workshops).	$3,000–$5,000 or partner-funded	Partner-led events; “bring ICP into a room.”

What You Get:

• ✅ Live (or virtual) session with agenda, exercises, and Q&A
• ✅ Leave-behind: slides, checklist, or short playbook
• ✅ Optional: recorded walkthrough for async review

Reference: Building an AI-Native Agency (free 90-min Cursor workshop → “We Build It for You”); SERVICES_MAP.md (Training & Enablement gap); SOWs that include “Cursor Enablement Workshop,” 90-min training, design workshops (e.g. Lilo, CTA, GA4 marine).

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Multi-session / 2-week training programs

Format	What we cover	Typical price	When we use it
2-week training program	Data literacy, analytics for PMs/analysts, or tool-specific certification-style training. Multiple sessions, homework, and light deliverables.	$10,000–$15,000	Client wants “everyone up to speed” on data or a tool before/after implementation.
Design workshops (embedded in projects)	1–2 design workshops with Product/CS to map workflows, define events, align on success metrics. Often part of a larger SOW (e.g. product analytics, BI migration).	Priced within project	Discovery/design phase of Product Analytics or BI work.

What You Get:

• ✅ Scheduled sessions with clear learning outcomes
• ✅ Materials, exercises, and optional certification-style checkpoints
• ✅ Documentation so the client can run future onboarding internally

Reference: SERVICES_MAP.md (2-week program $10K–15K); Trek SOW (design workshops); LMNT discovery (discovery workshops).

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Ongoing enablement retainer

Format	What we cover	Typical price	When we use it
Enablement retainer	Regular training cadence, office hours, “train the trainer,” and updates as tools or playbooks change. Keeps adoption high after go-live.	$5,000/month	Client has launched a platform or stack and wants sustained adoption without a full delivery retainer.

What You Get:

• ✅ Fixed cadence (e.g. biweekly office hours, monthly training slot)
• ✅ Updated runbooks, short recordings, or FAQs as things change
• ✅ Focus on adoption and self-serve, not net-new build

Reference: SERVICES_MAP.md (Ongoing Enablement Retainer $5K/mo); retainer add-ons in SOWs (e.g. “training” or “enablement” as a line item).

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How this ties to other services

• Free workshop → lead gen; can convert to AI Copilot / Knowledge Base (“We Build It for You”) or paid workshop for deeper training.
• Workshops and programs are often sold as add-ons to implementation (e.g. “90-min handoff + recorded walkthrough” in GA4 or Cursor SOWs).
• Enablement retainer can follow a Full Data Platform, Product Analytics, or AI Copilot engagement so the client keeps adoption high without re-scoping a full delivery retainer.

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📊 Service Comparison Matrix

Service	Price Range	Duration	Deliverables	Best For
Data Audit (per function)	$5,000-10,000	1-4 weeks	Audit memo/report, recommendations	Marketing, Product, or dbt assessment
Data Tool / Full Stack	$5,000+ per layer	2-6 weeks	Tool(s) or layer(s) implemented, docs	Single layer or full stack
AI Copilot / Context Graph	$7,500+	1-2 weeks+	Knowledge base, context graph pilot, automations	AI-native workflows, agencies
Full Data Platform	$15K-25Kdiscovery;$40K-150K+ build	3 wks-12 mo	Data map, dictionary, architecture, phased build	From-the-ground-up (e.g. health, multi-LOB)
ETL Setup	$15,000-30,000	3-4 weeks	ETL architecture, POC	Data integration
Data Warehouse	$20,000-40,000	4-6 weeks	Warehouse + dbt, docs	Centralized data
Product Analytics	$25,000-50,000	6-8 weeks	Full analytics platform	Product insights
Platform Build	$75,000-200,000+	8-16 weeks	Custom platform	Unique needs
Retainer	$10,000-40,000/mo	Ongoing	Continuous support	Ongoing needs
Training & Enablement	$3K-15K/$5K/mo	0.5 day-2 weeks / ongoing	Workshops, programs, enablement	Adoption, data literacy, AI workflows

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🎯 Service Selection Guide

By Use Case

“We need to audit our data” → Data Audit & Tracking Validation, function-specific ($5,000-10,000 per function; Marketing, Product, or dbt)

“We want to implement [tool] or layers of the stack” → Data Tool Implementation & Full Data Stack ($5,000+ per layer)

“We need AI that uses our knowledge / context” → AI Copilot Integration Sprint & Custom Context Graphs ($7,500+)

“We need to stand up a full data platform from scratch” → Full Data Platform Assessment & Implementation (discovery $15K-25K; build phased)

“We want a data warehouse” → Data Warehouse Setup ($20,000-40,000)

“We need product analytics” → Product Analytics Platform ($25,000-50,000)

“We want to build a platform” → Tech-Enabled Platform Build ($75,000-200,000+)

“We need ongoing support” → Full-Service Data Management ($10,000+/month)

“We want to hire someone” → Dedicated Resource Retainer ($16,000-40,000/month)

“We need training / enablement” → Training & Enablement (workshop $3K-5K,program$10K-15K, enablement retainer $5K/mo); free 90-min Cursor/knowledge-base workshop for agencies as lead-gen

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📞 Next Steps

Ready to discuss? Email sales@brainforge.ai with:

1. Service(s) you’re interested in
2. Your current situation
3. Timeline and budget range
4. Key stakeholders involved

Need a custom quote? See PRICING_CALCULATOR.md

Have questions? See RATE_CARD.md for detailed pricing

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Last Updated: 2026-02-23
Version: 3.0