Brand Voice Content Engine

For

Marketing leads at B2B SaaS companies

Solves

Expert teams lack the hours to publish, resulting in empty calendars and disengaged audiences.

Delivers

Scheduled blog posts, social media threads, newsletters, performance reports

Built in

about 2 weeks of creation time, MVP in 2 days

Investment

$5,500 for the MVP, $17,500 for the full product

Run it

Inside your business, or as part of your offer to clients

For marketing leads at B2B SaaS companies, turn internal documents and call recordings into scheduled blog posts and social threads.

1. Ingest and index brand assets.
2. Analyse tone and key messages.
3. Generate drafts for multiple channels.
4. Schedule posts across platforms.
5. Monitor engagement metrics.
6. Flag drafts for human review.

What goes in, what comes out

The workflow

1. In
   Start with

   Company documents, product pages, call recordings, past content, brand guidelines

2. 1

   Upload brand assets

3. 2

   Define tone and channels

4. 3

   Review AI drafts

5. 4

   Approve or edit

6. 5

   Schedule publication

7. 6

   Analyse performance

8. Out
   Finish with

   Scheduled blog posts, social media threads, newsletters, performance reports

AI does the heavy lifting, people stay in charge

Use language models to synthesise information from documents and recordings, maintaining brand voice consistency. The AI drafts content and suggests scheduling based on engagement patterns. A human editor verifies factual accuracy and tone before publication.

What your team sees

Key screens: Knowledge ingestion, content draft board, performance dashboard. Use a central feed for drafts, a side panel for brand voice guidelines and source documents, and a performance graph for engagement metrics. Display approval status clearly. In this product, the first view is the ingestion dashboard, followed by the draft board and performance dashboard.

Accounts and administration

User roles, draft versions, approval workflows, audit logs, publishing permissions

Integrations and data access

CMS, social media platforms, email marketing tools

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   2 days

   One buyer segment, one recurring use case. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   3 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   6 days

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will marketing leads at B2B SaaS companies use it to solve "expert teams lack the hours to publish, resulting in empty calendars and disengaged audiences"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Compare output volume against manual production over 4 weeks to prove time savings.
4. Measure, then decide. Track number of posts published and engagement rate increase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. One B2B SaaS client, focus on blog posts and LinkedIn updates, manual review of drafts, ingestion of website and 5 core PDFs

After the MVP. Automated scheduling, performance-based content adjustment, multi-language generation

What the build depends on. Large language models with long context windows

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

$2,500 per month

Message to test

Turn your internal knowledge into a publishing machine

Where to find buyers

LinkedIn, industry forums, marketing conferences

Lead magnet

Free audit of current content backlog

The first 30 days

1. Week 1: Ingest website and 5 core documents.
2. Week 2: Generate 3 blog posts and 10 social updates.
3. Week 3: Set up approval workflow.
4. Week 4: Launch first scheduled posts.

Paid pilot

Compare output volume against manual production over 4 weeks to prove time savings.

Success metrics

Number of posts published, engagement rate increase

Retention and expansion

Monthly subscription for ongoing content generation and optimisation

Why clients would pick it

Accumulation of unique brand data and fine-tuned models makes it harder for competitors to replicate the specific output quality

Alternatives and positioning

Manual writing, generic AI copy tools, content agencies

Main delivery costs

AI inference costs, developer time for setup

Fact-checking against source documents, tone guardrails, human approval gate