Frequently Asked Questions

Frntir works primarily with mid-sized businesses of 50 to 500 employees. Our AI solutions are purpose-built for organisations large enough to have complex knowledge and processes, but without the in-house AI teams that enterprises rely on.

Generic AI tools give generic answers. Frntir trains AI on your own documents, configures it with your brand voice and business rules, and deploys it into your actual workflows. Every answer is sourced from your knowledge base, not the open internet.

Frntir offers seven ready-to-deploy solutions: Document Search, Website Chat, Market Intelligence, Virtual Representative, Meeting Assistant, Interviews and Data Collection, and Workflow Automation. Custom solutions are also available. All run on a single unified platform trained on your organisation's own knowledge.

Google Workspace (including Google Drive), Slack, Microsoft SharePoint, and Microsoft OneDrive integration comes standard. The AI imports documents from your cloud storage, joins meetings from your calendar, and works across your website, email, and chat channels. Other integrations are added regularly to fit around your existing stack.

Book a free call to discuss which AI solution fits your business. Frntir will scope the opportunity, recommend an approach, and show you how quickly you can move from conversation to a working solution.

Agentic AI systems go beyond basic automation by making decisions within defined boundaries, taking action across multiple tools and data sources, and escalating to humans when needed. Unlike rule-based automation, Agentic AI understands context, handles exceptions, and operates across interconnected systems. That capability demands more from your organisation, not less.

The root causes are implementation problems, not technology problems. 43% of failures stem from data quality issues, followed by misalignment with business processes, skills shortages affecting 57% of companies, and cost overruns that hit 85% of projects. Most organisations spend months evaluating models but only weeks on change management. While 78% of companies now use AI in some form, 78% also report no bottom-line impact.

Research shows that 70% of AI implementation challenges stem from people and process issues, 20% from technology, and only 10% from algorithms. Most companies invert this completely, spending months on model selection and weeks on change management. Organisations that understand this distinction generate 1.7x more revenue growth and 1.6x higher margins than competitors.

There are three layers to address before any AI initiative. First, broken processes: steps that exist for historical reasons, handoffs that create delays, duplication that wastes effort. Second, operating model gaps: unclear decision ownership, conflicting departmental metrics, diffuse accountability. Third, data foundation: inconsistent definitions across systems, data re-entered into multiple tools, and reconciliation happening in spreadsheets.

Critical. 68% of companies cite lack of access to quality data as a top blocker. Organisations should earmark 50–70% of their timeline and budget for data readiness, including extraction, normalisation, governance, and quality assurance. This is not glamorous work, but it is the work that determines whether an AI project succeeds or joins the 80% that fail.

AI amplifies what is already there, good or bad. If your processes are clean, AI makes them faster. If your data is consistent, AI makes it useful. But if processes are tangled, AI tangles them at machine speed. If data lives in silos, AI hallucinates to fill the gaps. The rule is simple: fix before you automate.

Evaluate four dimensions. Data readiness: can you access clean, integrated data across key systems with consistent definitions? Process readiness: have you mapped the target workflow end-to-end as it actually operates and quantified its cost? Organisational readiness: is there clear ownership, communicated strategy, and budget for change management? Governance readiness: have you defined decision boundaries, escalation paths, and data privacy requirements?

Because every solution runs on the same proven platform, deployment is faster than building from scratch. Timelines depend on the complexity of your knowledge base and integrations, but most solutions move from scoping to live in weeks, not months.

There are four main routes. Build in-house: 12–24 months, high cost, only 22% success rate. Off-the-shelf solutions: fastest but limited customisation. Platform plus configuration: 3–12 months, a flexible middle ground. Consultancy-led implementation: accelerated timeline through proven methodology, with knowledge transfer to your team. The research is clear that 70% of AI success comes from people and process factors, which is why expert guidance makes a measurable difference.

Start with a workflow that matters (not your most complex or political one). Map it end-to-end as it actually works, not as it is supposed to work. Fix the process before automating it. Define success metrics with specific numbers. Run a controlled pilot with a small team for 30 days and measure everything. Then scale systematically. The journey from prototype to production averages 8 months for projects that succeed.

62% of CEOs now expect AI ROI in 3–5 years rather than 1–3 years. Meaningful operational integration typically requires 18–36 months. However, specific use cases can deliver faster returns: customer service automation in 3–6 months, scheduling and recruitment screening in 1–3 months, and document processing often pays back within months.

Yes, the AI is trained exclusively on your organisation's documents and knowledge. Your data is not shared with other clients or used to train public models. Every interaction is logged with a full audit trail for transparency and compliance.

Yes. Every AI interaction is fully logged, including what was asked, what was answered, and what knowledge was used. This audit trail is designed for regulated industries where accountability and traceability are non-negotiable.

Agentic AI requires four prerequisites: end-to-end visibility across systems so the agent is not guessing, clear decision boundaries defining when it acts autonomously versus escalates, consistent definitions of business concepts across departments, and responsible AI guardrails. 72% of companies already report unmanaged AI security risks. Companies succeeding with AI are 5x more likely to have these guardrails in place.

AI Theatre means pursuing visible, customer-facing AI implementations to impress rather than back-end optimisation that delivers returns. The average organisation launches 37 proof-of-concepts; only 3 are considered successful. The antidote: focus on boring operational problems that cost real money. Document processing delivers 30–40% time savings. Invoice matching pays back in months. Customer-facing innovation can wait until your foundations are solid.

Starting with “we need AI” and searching for applications gets it backwards. Before any AI initiative, ask three questions: can we describe how we will measure success? Do we have clean, accessible data for this use case with clear ownership? Have we fixed the underlying process, or are we automating something broken? If you cannot answer yes to all three, focus on your business model and operations before investing in AI.

A decisive one. 45% of CEOs report most employees are resistant or hostile to AI, 75% of employees worry it could eliminate jobs, and only 15% of organisations have communicated a clear AI strategy to their people. The solution: involve 21–30% of employees in transformation efforts rather than the typical 2%, communicate strategy clearly, invest in training, create AI champions from influential employees, and embed AI in existing workflows rather than offering it as a separate tool.

Industries seeing strong results include financial services (77% report positive returns in year one, 20x speed improvement in claims processing), healthcare (£3.20 return for every £1 invested within 14 months), legal and professional services (70–90% reduction in contract review time), manufacturing (74% of executives see returns within year one), retail (top performers achieve 8x return on customer service automation), and recruitment (£2,300 saved per hire with 792 hours recovered annually).

ROI varies by use case. Document processing delivers 30–40% time savings. Customer service automation achieves 52% faster ticket resolution with 90% containment rates. Predictive maintenance reduces unplanned downtime by 35%. Recruitment screening reduces hiring costs by 30% and fills positions 40% faster. Time to break-even ranges from 1–3 months for recruitment to 6–12 months for financial services.