AI in Production

GenAI is not just another model. It’s a system that touches sensitive data, generates content in real time, and needs governance built in from day one. Without that, demos collapse under production pressures: hallucinations, latency spikes, compliance gaps, and user trust issues.

Recent reports from Forrester and McKinsey highlight the gap: while 70% of companies are experimenting with GenAI, fewer than 15% have deployed it into core business processes at scale. The blocker isn’t creativity – it’s the missing scaffolding around the models.

At Futurice, we help organisations make the leap. We don’t just test prompts – we have launched live, production-grade GenAI systems that balance speed, safety, and sustainability.

Financial Services - transaction intelligence & knowledge assistants

Banks are exploring GenAI to speed up RFP responses, generate investor insights, or power internal knowledge search. The production challenge isn’t “can the model answer?” It’s:

• How do we ground answers in approved documents?
• How do we enforce permissions and audit trails?
• How do we manage latency and cost when the system scales to thousands of users?

That means retrieval pipelines with hybrid search, grounded RAG architectures, continuous evaluation, and monitoring for bias or drift.

Legal - due diligence & complex contracts

Law firms see promise in GenAI for summarising due diligence bundles or extracting obligations from contracts. But production-grade means:

• Every output must include provenance and citations.
• Confidential data must stay within secure environments.
• Workflows must enforce human-in-the-loop review before client delivery.

This is where frameworks like LlamaIndex or LangGraph can be paired with evaluation tools to keep outputs auditable, and monitoring frameworks to flag hallucinations or missing clauses before they reach a lawyer’s desk.

Media & Publishing - real-time news and fact-checking

Newsrooms experiment with GenAI for content tagging, headline suggestions, and fact-checking. At production scale, the questions shift:

• How do we ingest and process real-time feeds?
• How do we design editorial override interfaces so humans stay in control?
• How do we prove content provenance to audiences?

This is where architectures like GraphRAG shine - building entity-event graphs to improve accuracy - and where standards like C2PA/Content Credentials can anchor trust.

It’s not just about a model that runs. It’s about a system that:

• Monitors itself: hallucination detection, drift alerts, latency tracking.
• Explains itself: provenance, citations, transparent logs.
• Controls itself: kill-switches, rollbacks, human-in-the-loop gates.
• Adapts: architectures that support swapping models or retrieval layers as tech and regulation change.

Because GenAI is unpredictable by design. Pilots are fun, but production-grade requires discipline: engineering, governance, and culture change. We bring:

• Hands-on experience with the frameworks and APIs that matter.
• Sector knowledge to know where compliance and trust will be tested.
• Future-proofing so today’s stack can evolve tomorrow.

We make GenAI boring in all the right ways - reliable, auditable, and ready for prime time.

Get in touch with us to discuss your requirements and a tailored approach to developing production-grade AI for your business.

References & further reading

• McKinsey – The State of AI Adoption, 2025 (fewer than 15% of GenAI pilots scaled to production)
• Forrester – Predictions 2025: Generative AI Will Be Everywhere, But Few Deployments Will Scale.
• Microsoft Research: GraphRAG approach for query-focused summarisation.
• C2PA / Content Credentials: Open standard for media provenance.