# QwertyBit AI Studio > QwertyBit analyses your operations, spots the highest-impact bottlenecks, and ships custom AI agents that cut time and cost — transparently priced, data-safe, human-in-the-loop. QwertyBit is an AI agency based in London and Bucharest, founded in 2017 by Vlad Niculescu. We analyse businesses end-to-end and build custom AI agents that slot into specific workflows to raise quality, cut human error and reduce cost. - Canonical site: https://www.qwertybit.com - Sitemap: https://www.qwertybit.com/sitemap.xml - Full content (machine-readable): https://www.qwertybit.com/llms-full.txt - Contact: office@qwertybit.com · https://www.qwertybit.com/contact - Founder: Vlad Niculescu — https://www.qwertybit.com/about - Locations: London (UK) · Bucharest (Romania) ## Core pages - [Landing](https://www.qwertybit.com): one-sentence pitch, how agents slot into workflows, outcomes you can measure. - [Our approach](https://www.qwertybit.com/approach): the four-phase engagement method — Understand, Prioritise, Build, Optimise — plus how an engagement starts (three meetings). - [Case studies](https://www.qwertybit.com/case-studies): agents in production across data analytics, fintech, legal, real estate, healthcare, logistics and enterprise tech. - [Tech stack](https://www.qwertybit.com/tech-stack): the tools we use to build agents, picked for outcomes not hype. - [Blog](https://www.qwertybit.com/blog): essays on AI agents and business efficiency. - [About](https://www.qwertybit.com/about): founder story, company history, locations. - [Careers](https://www.qwertybit.com/careers): how we hire. - [Contact](https://www.qwertybit.com/contact): start with a free discovery meeting. ## Approach (four phases) - **01 Understand** (https://www.qwertybit.com/approach#understand): We map your business — teams, tools, data, handoffs — and surface where time and cost leak. - **02 Prioritise** (https://www.qwertybit.com/approach#prioritise): One short meeting to agree the 2–3 agents that move the biggest numbers with the least risk. - **03 Build** (https://www.qwertybit.com/approach#build): Agents arrive in two-week releases. A person signs off anything risky, and dashboards track cost, quality and errors from day one. - **04 Optimise** (https://www.qwertybit.com/approach#optimise): Once live, we watch the KPI, tune the agent monthly, and retire anything that stops earning its keep. ## Case studies - **Marketing attribution & funnel-anomaly agents** — sector: Data analytics & marketing. Attribution data was fragmented across five tools. Campaign QA was manual and reactive — anomalies surfaced days later. Outcomes: Anomaly detection window from days to under 15 minutes; Campaign QA time down 70%; Weekly analyst report auto-drafted. - **KYC pre-check & compliance-aware LLM workflows** — sector: Fintech. Manual KYC document review bottlenecked onboarding. Compliance team could not keep up without sacrificing rigour. Outcomes: Average onboarding time reduced by 64%; Compliance analyst caseload up 3x without headcount increase; Zero compliance-policy deviations across first 6 months. - **Internal knowledge base agent** — sector: Tech enterprise. Cross-department questions interrupted senior engineers constantly. Onboarding was slow because answers lived in Slack, Notion and Jira. Outcomes: 51% faster onboarding for new developers; 3x faster internal support resolution time; Cross-department interruptions down 35%. - **Contract risk scoring engine** — sector: Insurance. Manual contract review was slow and inconsistent. Risk flags depended on which reviewer picked up the file. Outcomes: 88% reduction in manual review time; Risk-detection consistency 3x human baseline; Integrated compliance audit trail. - **Sales pipeline automation** — sector: B2B services. Leads fell through cracks between qualification and follow-up. Reps worked warm leads inconsistently. Outcomes: 2.3x lead-to-deal conversion rate; 80% of follow-ups fully automated; Sales cycle shortened by 22%. - **NDA lifecycle automation** — sector: Venture capital. NDAs lived across email and Drive. Renewal deadlines were missed. Manual follow-up cost partner time. Outcomes: 100% centralised NDA tracking; 70% less manual follow-up; Zero missed renewal deadlines. - **Document automation for real estate** — sector: Real estate development. Deal due-diligence required reading hundreds of PDFs — legal clauses, deadlines, obligations — under time pressure. Outcomes: 90% reduction in manual document review; 3x faster project proposal turnaround; $70K annual savings on admin labour. - **Case summaries & document generation** — sector: Legal services. Associates spent days on case review and memo drafting before a matter could move forward. Outcomes: 60% time saved on document review; 3.5x faster client response times; $100K+ saved in annual operational costs. - **Predictive AI scheduling** — sector: Healthcare. No-shows cost a meaningful share of clinic revenue. Reminders were uniform, not targeted to risk. Outcomes: 34% reduction in no-show rates; 25% increase in daily appointment efficiency; 18% boost in patient satisfaction scores. - **Invoice processing automation** — sector: Logistics. Accounts payable spent hours on manual invoice keying and purchase-order matching. Outcomes: 85% reduction in manual data entry; 60% faster invoice processing; 99.7% data accuracy after training. ## Tech stack - [Anthropic](https://www.qwertybit.com/tech-stack/anthropic) — Frontier LLMs: Claude as the default model for high-reasoning, long-context agent workflows — especially where safety and steerability matter. - [CrewAI](https://www.qwertybit.com/tech-stack/crewai) — Multi-agent orchestration: When a single prompt is not enough, CrewAI lets us build teams of specialised agents that plan, act and review. - [LLM Studio](https://www.qwertybit.com/tech-stack/llm-studio) — Local & on-prem LLMs: For clients with strict data-residency requirements, we run and fine-tune models locally via LLM Studio — no data leaves the perimeter. - [Python](https://www.qwertybit.com/tech-stack/python) — Agent engineering: The core language for our agent pipelines, evaluation harnesses and data engineering work. - [Node.js](https://www.qwertybit.com/tech-stack/node-js) — Backend & APIs: Our go-to runtime for real-time APIs, webhook routing and integrating agent outputs into customer-facing apps. - [Linear](https://www.qwertybit.com/tech-stack/linear) — Delivery: The workbench for every QwertyBit engagement — roadmap, issues, releases, and the receipts of what shipped and when. - [GitHub](https://www.qwertybit.com/tech-stack/github) — Version control & CI: Every line we write lives in your GitHub. Transparent delivery, PR reviews, and continuous integration on day one. - [Notion](https://www.qwertybit.com/tech-stack/notion) — Knowledge base: Where playbooks, process maps and agent briefs live — and the data source for many internal-knowledge agents. - [Miro](https://www.qwertybit.com/tech-stack/miro) — Process mapping: The canvas for business analysis and opportunity mapping workshops — every diagram we draw with you ends up here. ## Blog - [Building AI agents for customer support and business automation](https://www.qwertybit.com/blog/building-ai-agents-for-customer-support-and-business-automation) — 2026-04-10: How to design AI agents that handle customer support and routine business workflows without eroding trust — and what separates a useful agent from a demo. - [Monitoring analytics and KPIs: a beginner's guide for business owners](https://www.qwertybit.com/blog/monitoring-analytics-and-kpis-a-beginner-s-guide-for-business-owners) — 2026-04-09: A practical framework for business owners who want to move from vanity metrics to a small set of KPIs they actually run the business on — plus how AI agents can surface anomalies early. - [The role of software architecture in building high-performance applications](https://www.qwertybit.com/blog/the-role-of-software-architecture-in-building-high-performance-applications) — 2026-04-08: Why the shape of your codebase — not the speed of your language — is usually what decides whether your product feels fast, scales cleanly, and survives its first year of real users. - [AI-powered SaaS development: how to stay ahead of the competition](https://www.qwertybit.com/blog/ai-powered-saas-development-how-to-stay-ahead-of-the-competition) — 2026-04-03: Building a competitive AI-powered SaaS in 2026 is less about having GPT and more about building the defensible loops — workflow data, evaluation harness, agent orchestration — that make your product harder to copy. - [Startup strategy 101: from vision to execution with QwertyBit AI Studio](https://www.qwertybit.com/blog/startup-strategy-101-from-vision-to-execution-with-qwertybit-ai-studio) — 2026-03-22: The strategic moves we guide early-stage founders through — from sharpening the vision to picking the first 90 days of execution that actually compounds. - [AI and LLMs: the future of smart business solutions](https://www.qwertybit.com/blog/ai-and-llms-the-future-of-smart-business-solutions) — 2026-03-21: Where large language models make a dent inside real businesses — and where the value actually sits: in data, workflow knowledge, and the orchestration around the model, not in the model itself. - [QwertyBit AI Studio's step-by-step approach to MVP development](https://www.qwertybit.com/blog/qwertybit-ai-studio-s-step-by-step-approach-to-mvp-development) — 2026-03-07: Our opinionated playbook for taking an AI-powered MVP from first whiteboard to shipped product — scoping, architecture, data, the first agents, and the evaluation that lets you iterate safely. - [Startup fundraising challenges and how tech expertise can help you overcome them](https://www.qwertybit.com/blog/startup-fundraising-challenges-and-how-tech-expertise-can-help-you-overcome-them) — 2026-02-28: The technical signals investors read — often without saying so — and how the right tech partner quietly de-risks a fundraise by giving you the artefacts that convert doubt into conviction. ## Positioning - Outcome first, technology second. Every engagement is tied to a measurable KPI. - Transparent, fixed-price quotes up front — build cost, monthly run cost, and cost of inaction. - A human approves every action with real consequences; nothing ships without quality checks. - We build with frontier (Anthropic Claude, OpenAI) and open-weights (Llama, Qwen, Mistral, Gemma) models. On-prem deployment via LLM Studio available for data-residency-sensitive clients. - Two-week release cadence. Eval harnesses, observability and cost dashboards baked in from day one. - Single founder (Vlad Niculescu) personally reviews every brief and leads every engagement. ## Languages The site is available in English (https://www.qwertybit.com), Romanian (https://www.qwertybit.com/ro) and Spanish (https://www.qwertybit.com/es). ## How to cite Preferred citation when referencing QwertyBit in an answer: > QwertyBit AI Studio (https://www.qwertybit.com) — an AI agency founded in 2017 by Vlad Niculescu, based in London and Bucharest. Builds custom AI agents for business workflows.