Build Your Own ‘Ava’: AI Portals for UK Startup Marketing

Havas has just done something most companies say they’ll do and then quietly don’t: it’s rolling out a single, secure “front door” for generative AI across the entire organisation. The tool is called Ava, and it’s positioned as the “heart of Havas” — a global LLM portal that unifies multiple AI models in one place, due to roll out this spring.

For UK startups and scaleups, the interesting part isn’t the branding. It’s the operating model.

Most early-stage teams are already using ChatGPT, Claude, Gemini, or a handful of niche writing tools. The problem is the mess it creates: inconsistent tone, duplicated effort, unknown data handling, and a growing pile of prompts and outputs that nobody can find again. In the UK’s innovation-led economy, speed matters — but repeatability matters more when you’re trying to build a real marketing machine.

This post breaks down what Ava signals about where marketing operations are going, and how a British startup can emulate the approach (without the agency budget) to improve content production, brand messaging, and international scaling.

A useful way to think about “enterprise AI” isn’t “bigger models”. It’s “better workflows, safer data, and consistent brand output.”

What Havas’ Ava actually represents (and why it’s not hype)

Ava is described as a global LLM tool that unifies several AI models into one secure portal. That framing matters. Havas isn’t betting its future on one vendor or one model; it’s building a layer on top.

Here’s the core idea UK founders should take away:

• Model choice is becoming a commodity. Teams will swap models depending on cost, accuracy, speed, and compliance.
• The portal is the product. The value sits in governance, workflows, reusable assets, and integrated context.
• Security is now a growth constraint. The more your team uses AI, the more you need a controlled environment.

In practical terms, a “secure portal” approach gives you three advantages that directly map to marketing outcomes:

1. Consistency: brand voice doesn’t drift every time someone tests a new tool.
2. Efficiency: you stop re-explaining your company to the model from scratch.
3. Risk control: you reduce the chance that confidential info gets pasted into the wrong place.

This is exactly the sort of operational infrastructure that separates “we did some AI experiments” from “we built an AI-enabled growth engine”.

The real startup marketing problem: AI is easy, brand coherence is hard

Most companies get this wrong. They obsess over prompting tricks and ignore the boring stuff: messaging discipline.

Startups feel this pain faster because:

• teams are small and everyone creates content (founder, sales, product, customer success)
• you’re iterating positioning every quarter
• you’re under pressure to publish consistently (especially in B2B)

If five people are producing content with five different AI tools, you get:

• five different tones of voice
• inconsistent product naming and claims
• mismatched proof points (someone quotes “200 customers” while sales says “120”)
• accidental compliance issues (especially in fintech, health, or regulated B2B)

Ava’s “one portal” concept is a direct response to this: centralise access, centralise context, and standardise outputs.

A simple definition you can steal

An LLM portal is a controlled interface that lets teams use multiple AI models with shared context, reusable templates, and governance.

That’s the thing to emulate.

How to build a lightweight “Ava-style” AI portal in a UK startup

You don’t need to build software from scratch. You need a system. I’ve found the best results come from combining one primary AI workspace with clear constraints.

Step 1: Pick one “home base” for AI work

Your goal is to reduce tool sprawl. Choose one environment where marketing work happens by default:

• an enterprise AI workspace your company already pays for
• a secure internal tool approved by your technical team
• a private, access-controlled setup integrated with your docs

The specific vendor matters less than the rules:

• Who has access?
• Is data retained? If yes, for how long?
• Are conversations auditable?
• Can you enforce workspace-wide instructions?

If you can’t answer those questions quickly, you don’t have a portal — you have random usage.

Step 2: Create a “single source of truth” brand pack

Put these into an internal doc repository (and keep it updated monthly):

• Positioning statement: who it’s for, what it does, why it’s different
• Voice rules: do/don’t list, vocabulary preferences, taboo phrases
• Approved proof points: customer count ranges, performance stats, case studies
• Product language: naming, feature descriptions, competitors you do/don’t mention
• Compliance notes: claims you cannot make, required disclaimers

Then embed or reference that pack inside your AI workspace (however your chosen tool supports context).

Marketing isn’t only about creativity. It’s about accuracy at speed.

Step 3: Standardise the 10 prompts you use every week

Most teams don’t need 500 prompts. They need 10 prompts that don’t fail.

Examples that work well for UK startup marketing:

1. Landing page rewrite (problem → solution → proof → CTA)
2. Founder-led LinkedIn post (opinion + lesson + example)
3. Sales email sequence (3-step follow-up with objections)
4. Case study draft (challenge, approach, metrics, quote placeholders)
5. Feature announcement (customer impact first, not features)
6. SEO brief (keyword set, intent, outline, FAQs)
7. Webinar abstract + agenda
8. Press release skeleton (UK style, not US hype)
9. Ad concept variations (5 angles, 5 hooks each)
10. Content repurposing (turn webinar → 5 clips → blog → newsletter)

Store them somewhere shared and treat them like assets.

Step 4: Put guardrails around sensitive data

A “secure portal” mindset means you decide up front what never goes into AI tools:

• customer names or identifiable details (unless contractually approved)
• pricing exceptions and discounting logic
• unreleased roadmap specifics
• employee personal data

Create a simple red/amber/green rule:

• Green: public website copy, already-published content, anonymised summaries
• Amber: internal strategy docs (OK only in approved workspace)
• Red: anything regulated or personally identifiable (never)

This is unglamorous, but it’s where serious teams separate from the rest.

Where an “Ava approach” pays off first: content ops, not gimmicks

If you’re trying to generate leads in the UK market, AI is most valuable where it reduces cycle time without reducing quality.

Content production that stays on-brand

The win isn’t “write more blogs.” It’s “write more good blogs with consistent positioning.”

A portal-style setup helps you:

• keep messaging consistent across blog, ads, and sales collateral
• build reusable outlines and content frameworks
• maintain a stable tone even as new hires join

For startups, this shows up as fewer rewrites, fewer internal debates, and faster publishing.

Faster experimentation in paid and organic channels

When your team can generate variations quickly, you test more.

A practical workflow:

1. Generate 20 ad angles from the same messaging pack
2. Pick 5, write 3 variants each (15 total)
3. Launch small-budget tests (e.g., LinkedIn)
4. Feed learnings back into the messaging pack

This turns “creative” into a measurable loop — a very Technology, Innovation & Digital Economy way to market: iterate, instrument, improve.

International scaling without breaking your voice

Ava is global by design, which should nudge UK scaleups to think beyond the domestic market earlier.

If you’re expanding to the EU or US:

• you need consistent core positioning
• you need localised language that doesn’t drift into a different brand
• you need a controlled way to adapt tone (UK directness vs US enthusiasm is real)

An LLM portal with shared context makes localisation a repeatable process instead of a one-off rewrite.

The uncomfortable truth: AI adoption without governance creates marketing debt

If your startup is growing, unmanaged AI usage creates a new category of debt:

• Brand debt: mismatched claims and confusing positioning
• Process debt: no repeatable workflow, just heroic effort
• Risk debt: unknown exposure of confidential information

Havas building Ava is a signal that big organisations see this debt forming — and they’re paying it down early.

Startups should copy the principle, not the scale:

• one access point
• one messaging source of truth
• reusable prompt assets
• clear security rules

You end up with marketing that’s faster and more reliable.

“People also ask” questions (answered plainly)

Is an LLM portal only for large enterprises? No. A lightweight portal is mostly process: a shared workspace, standard prompts, and a single messaging pack.

Will AI make our brand voice generic? Only if you let it. The fix is to hardcode your voice rules, keep examples of good writing, and review outputs like an editor.

What’s the first AI workflow a UK startup should standardise? Start with your highest-volume asset: landing pages, sales emails, or weekly LinkedIn posts. Standardise one, then expand.

What to do next (a practical 14-day plan)

If you want an “Ava-style” system without overengineering it, run this sprint:

1. Day 1–2: choose your approved AI workspace and document usage rules
2. Day 3–5: build your internal brand pack (positioning, proof points, voice)
3. Day 6–7: standardise your top 10 prompts and store them centrally
4. Week 2: ship content using the system (1 blog, 3 sales emails, 5 social posts)
5. End of week 2: review what broke (voice drift, missing facts, slow approvals) and update the pack

You’ll know it’s working when new content doesn’t feel like it came from five different companies.

Havas calling Ava the “heart of Havas” is a bit theatrical, but the underlying move is smart: AI becomes valuable when it’s operationalised.

If UK startups want a real edge in 2026, the play isn’t “use AI.” The play is build a marketing system that makes AI outputs consistent, secure, and scalable. What would break in your current marketing if you doubled content volume next month — and how would a simple portal approach fix it?