Wizzro blends deep knowledge engineering with AI to digitize, structure, and activate your most critical information assets — delivering measurable outcomes at enterprise scale.
Trusted by leading organizations across 12+ countries
A structured, repeatable methodology designed to deliver results at every stage of the engagement.
We assess your knowledge assets, infrastructure, and goals to map exactly what transformation is possible and where to start.
We design and build a bespoke pipeline — OCR, NLP, structuring, translation, retrieval — tuned to your content and use case.
We maintain and evolve your systems — backed by SLA guarantees, idle-time zero-scaling, and a dedicated support team.
We deliver outcomes across knowledge transformation, enterprise AI, and end-to-end software — each reinforcing the other.
We digitize, structure, and activate static documents — archives, manuscripts, catalogues — into searchable, intelligent knowledge systems.
We embed AI intelligence into your operations — automating decisions, surfacing insights, and building assistants that accelerate your enterprise.
From concept to production, we build custom applications and provide dedicated engineering teams that scale with your ambitions.
Keep your critical data protected with enterprise-grade security, advanced access controls, and private deployment options.
Designed with the EU AI Act at its core, Wizzro simplifies documentation, auditing, and model risk governance across all deployments.
Deploy within an isolated private cloud to maintain rigorous compliance standards and data sovereignty at all times.
Ensure complete data sovereignty with air-gapped deployments securely housed behind your firewall. Zero external data transfer.
Integrate Wizzro into your existing stack — any infrastructure, any ML tool, any data source. No rip-and-replace.
Live dashboards, model accuracy tracking, and alerting built in. Idle-time zero-scaling minimizes cost without sacrificing availability.
Fine-grained permissions, SSO integration, and audit logs ensure the right people access the right knowledge at the right time.
From digitising cultural archives to translating multilingual curricula — we serve the sectors where knowledge quality determines outcomes.
A government cultural institution partnered with Wizzro to digitize, translate, and deploy a fully searchable RAG system — reducing research time by 80% and opening access in 40+ languages.
Read the full story →Why 74% of enterprise knowledge remains inaccessible — and what leading organizations are doing about it.
Download report →A practical guide to selecting the AI approach that fits your data, budget, and time horizon.
Read article →Talk to a Wizzro expert about your project. We respond within one business day.
Not just the Fortune 500. Not just those with million-dollar IT budgets. Every entrepreneur, every SME, every team still running on spreadsheets and paper trails — they deserve to compete in the age of AI too.
The businesses being left behind aren’t failing because they lack ambition. They’re failing because no one built the right tools for them — until now.
— Wizzro Founding PrincipleThousands of SMEs and mid-market businesses are running critical operations on paper trails, email chains, and manual spreadsheets. Not because they want to. Because the tools built for them were too complex, too expensive, or too generic to work.
Meanwhile, enterprise giants pour billions into AI-powered workflows. The gap isn’t closing — it’s widening. Every quarter a business delays digitising its workflows is another quarter ceding ground to competitors who have already made the leap.
We built Wizzro because this gap is not inevitable. It is a failure of imagination by the technology industry — and one we intend to fix.
Lost globally every year to manual, paper-based invoicing and the errors it generates.
Source: SOTI, 2024Average human error rate in any manual process — 10 mistakes per 100 steps, no matter how routine.
Source: SOTI, 2024Of SMEs say digital transformation is crucial for their business — yet most still haven’t started.
Source: Quixy, 2025Of data breaches originate from paper documents — lost, stolen, or never properly destroyed.
Source: SOTI, 2024Wizzro’s mission is to democratise access to intelligent workflow automation — making it practical, affordable, and genuinely useful for the SMEs and entrepreneurs who power economies but are chronically underserved by enterprise software.
We don’t build products for procurement committees and 18-month rollout plans. We build for the founder managing project delivery across 12 WhatsApp threads. For the agency manually processing client briefs in PDFs. For the logistics company running route planning on a whiteboard.
Deployment in weeks, not quarters. SaaS pricing that scales with your business, not against it.
Purpose-built RAG systems and automation tuned to how your business actually operates.
We integrate with what you have and digitise what you don’t — no rip-and-replace nightmares.
Hours saved per week. Error rates reduced. Projects on time. We build for results, not demos.
By 2030, we want to be the platform that made AI-powered workflows the default — not the exception — for SMEs across the Middle East, Africa, and beyond.
The same AI capabilities powering Amazon’s logistics — made accessible and affordable for businesses with 10 to 500 people.
Every manual form, every emailed PDF, every whiteboard workflow — replaced by intelligent digital systems that are faster, safer, and smarter.
We’re not just automating workflows — we’re building a generation of SME leaders who operate with the confidence of an enterprise.
They’re the decisions we make when it would be easier to cut corners — in pricing, in quality, in who we choose to serve.
Powerful technology shouldn’t require a seven-figure budget. We price for the businesses that have the most to gain and the least to waste.
We apply AI where it creates measurable value and say so honestly when a simpler solution is the right one.
Our SLA commitments, success fees, and retainer models exist because we’re betting on your outcomes, not just your signature.
Every system we deploy is designed to grow with your business — not lock you in. Openness and clean handoffs are non-negotiable.
We’re building Wizzro for the entrepreneur who knows their team is losing hours to admin that should take minutes. For the service business whose delivery process is held together by WhatsApp messages and hope. For the SME that wants to compete in the AI era but can’t afford to get it wrong.
You don’t need a $500K digital transformation consultant. You need a partner who understands your constraints, respects your budget, and delivers systems that actually work on Monday morning.
That’s what Wizzro is for.
A government cultural institution partnered with Wizzro and Thothica to digitize, translate, and deploy a fully searchable RAG system — reducing research time by 80% and opening access in 40+ languages.
A national cultural archive — home to over two million historical documents spanning five centuries — was facing an existential accessibility crisis. Researchers from around the world were requesting access to rare manuscripts, colonial-era administrative records, and multilingual correspondence that existed only in physical form, locked in climate-controlled storage facilities across three campuses.
The institution’s internal team had attempted digitization twice before, both times stalling due to the sheer volume of material, the diversity of scripts (Arabic, Ottoman Turkish, French, Portuguese, local dialects), and the absence of a system capable of making digitized content searchable at scale.
Each researcher visit required weeks of advance scheduling. A single document request could take 18 days to fulfill. International researchers were effectively locked out. The archive’s director described it plainly: “We are custodians of history that no one can access.”
Wizzro, in partnership with Thothica, designed a phased 14-week programme combining high-throughput scanning, multilingual OCR, semantic tagging, and a private RAG deployment hosted entirely on-premises within the institution’s own infrastructure.
Document audit, language profiling, OCR model calibration on a 5,000-document pilot batch.
High-throughput scanning pipeline, multilingual OCR at scale, semantic tagging and metadata enrichment.
Private on-premises RAG system, vector indexing, search UI, researcher access portal launch.
Staff training, documentation handoff, SLA-backed monitoring and ongoing model maintenance.
Within 30 days of go-live, the archive’s new AI knowledge system processed over 4,200 researcher queries — more than the institution had received in the previous three years combined. The average document retrieval time dropped from 18 days to under 90 seconds.
“Wizzro didn’t just digitize our archive. They gave it a voice. Scholars are discovering connections between documents that would have taken a lifetime to find manually.”
— Archive Director, National Cultural Heritage Institution
Why 74% of enterprise knowledge remains inaccessible — and what leading organizations are doing to close the gap. Based on interviews with 340 enterprise leaders across 18 countries.
Download Full Report (PDF) →Enterprise organizations are sitting on a crisis they can’t see clearly. Across the 340 organizations we surveyed, 74% reported that more than half of their operational knowledge is effectively inaccessible to the people who need it most.
This isn’t a technology problem. Every organization we interviewed had invested in knowledge management tools. The problem is structural: knowledge is created faster than it is organized, stored in formats that can’t be searched, and owned by individuals rather than systems.
of enterprise knowledge is effectively inaccessible to the employees who need it most.
Wizzro State of Enterprise Knowledge Report, 2025 · n=340On average, employees waste 18 minutes finding a single document they know exists. Across an organization of 200 people, this compounds to over 62,000 hours lost per year — the equivalent of 31 full-time employees doing nothing but searching.
67% of organizations store critical knowledge in formats that cannot be searched: scanned PDFs without OCR, handwritten notes, audio recordings, and images. Even organizations with modern document management systems are managing symptoms, not the cause.
Organizations that deployed RAG systems reported a 3.2× improvement in relevant document retrieval accuracy versus keyword search, and an 81% reduction in time-to-answer for researcher queries. The gap widens as document volume grows.
83% of organizations operating in more than one language region had not indexed their non-primary-language content at all. Existing knowledge translated and indexed into a unified retrieval system typically yields 40–60% additional unique insights.
“We had 14 years of institutional knowledge stored in formats no one could search. Fixing that in 14 weeks felt impossible until Wizzro showed us how.”
— Head of Digital Transformation, Regional Development Bank
A practical guide to selecting the AI approach that fits your data, budget, and time horizon — with a decision framework you can apply to your next project today.
Every week, we speak with organizations that have decided to “use AI” on their knowledge base — and almost every one leads with the same question: should we fine-tune a model on our data, or build a RAG system? The answer matters enormously. The wrong choice costs months and hundreds of thousands of dollars.
A base LLM is given real-time access to your documents via a vector search index. When a user asks a question, the system retrieves relevant passages and feeds them to the model as context. The model’s “knowledge” is always current — because it reads your documents fresh on every query.
An existing LLM is retrained on your organization’s data, adjusting its weights so it “internalizes” your content, style, and domain knowledge. The model itself changes — but it cannot access documents it wasn’t trained on, and retraining is required whenever your knowledge base changes.
Yes → RAG. Fine-tuning bakes knowledge into the model at a point in time. RAG reads your documents dynamically — add a new file, it’s immediately searchable.
No → Either can work. If your corpus is stable and you need highly specialized domain vocabulary, fine-tuning may add value on top of RAG.
Yes → RAG. When a RAG system answers a question, it knows exactly which documents it retrieved. A fine-tuned model cannot — its knowledge is encoded in weights, not traceable to a source document.
Tight → RAG. A production RAG system can be deployed in 2–6 weeks. Fine-tuning typically takes 3–6 months minimum and requires significant GPU compute cost.
Any size → RAG scales from under 10,000 to over 1 million documents with proper chunking and indexing. Fine-tuning on very large corpora is exceptionally expensive and rarely cost-effective.
Yes → Consider fine-tuning as a layer on top of RAG. If you need a model that responds in a very specific organizational voice or applies domain-specific reasoning, fine-tuning can help — but it rarely replaces the need for RAG for factual retrieval.
For 90% of organizations with an existing knowledge base, RAG is the right starting point. It is faster to deploy, easier to maintain, more transparent in its reasoning, and far more cost-effective to scale.
The most sophisticated knowledge systems we have deployed combine both: a RAG layer for factual retrieval and source citation, with a lightly fine-tuned output model that applies organizational style to the retrieved context. But start with RAG. Get it working. Prove the value. Then layer in fine-tuning where it genuinely adds something that retrieval cannot.
“Start with RAG. Prove the value. Then layer in fine-tuning where it genuinely adds something that retrieval cannot. In that order, almost every time.”
— Wizzro AI Engineering Team