AI RAG, Agents, & Avatars

Delivering practical AI for regulated teams, people operations, and customer-facing mobile products.

Case Study 1

Retrieval-Augmented Legal Guidance for Albanian Law

The Challenge

An Albanian law office needed instant, trustworthy answers across centuries of civil code, local regulations, and court rulings. Attorneys were spending hours cross-referencing the Official Gazette manually, and junior staff lacked confidence interpreting overlapping statutes.

The Solution

We built a RAG pipeline that embeds the full corpus of Albanian law, chunks content by jurisdiction, and grounds every prompt before the LLM drafts advice. Citations link directly to the Official Gazette so attorneys verify each recommendation against the controlling statute.

Daily ingestion jobs monitor new decrees, bar bulletins, and EU-aligned updates
Role-based access controls and audit logs protect client matters
On-premise deployment meets Albanian data residency requirements

Daily jobs embed new decrees and rulings. Every question retrieves the relevant law before the LLM drafts advice, and each answer cites the Official Gazette so attorneys can verify it against the controlling statute.

70%

reduction in legal research time per query — from hours of manual cross-referencing to minutes

100%

of answers include direct Gazette citations, giving attorneys an auditable trail for every recommendation

Daily

knowledge updates — new decrees and rulings are embedded automatically without downtime

Case Study 2

Scaling an HR Startup with Intelligent Agents

The Challenge

A fast-growing HR software startup was winning enterprise contracts faster than it could hire support staff. Customer ticket volumes tripled, onboarding requests queued for days, and the operations team was spending most of its time on repetitive data handoffs between systems.

The Solution

We layered specialised AI agents over the startup’s product and back-office stack. Agents triage incoming tickets, draft human-ready replies, orchestrate approval workflows between the SaaS platform, CRM, and finance systems, and surface edge cases to specialists with full context attached.

Smart intake agents classify and route tickets across HR, payroll, and compliance
Workflow agents automate multi-step approval chains end-to-end
Oversight dashboards track agent actions, KPIs, and customer health in real time

Intake agents classify and route every ticket across HR, payroll, and compliance. Workflow agents run approval chains between the SaaS platform, CRM, and finance systems, edge cases reach a specialist with full context attached, and oversight dashboards track every agent action.

ticket volume handled without adding headcount — agents absorb routine queries so the team focuses on high-value work

80%

of approval workflows fully automated — reducing manual handoff time from days to minutes

< 2 min

average first-response time for agent-triaged tickets, down from 4+ hours

Case Study 3

Building a Conversational AI MVP for a US Startup

The Challenge

A seed-stage US startup needed to prove a conversational AI concept before their next funding round. They had domain expertise but no AI engineering bench, and investors expected a working demo with real users — not a slide deck — within 10 weeks.

The Solution

We shipped a lean MVP that combines speech-to-text, retrieval-augmented prompts, and lightweight avatar animation so early adopters can query account data, receive guidance, and hand off to human support when needed.

Low-latency streaming keeps conversations responsive under 200ms
Sensitive fields are tokenised before hitting the LLM, meeting US data expectations
Product analytics track adoption, CSAT, and support deflection from day one

Speech is transcribed, sensitive fields are tokenised before they reach the LLM, and answers stream back through the avatar. Users can hand off to human support, and product analytics track adoption, CSAT, and support deflection from day one.

KickoffInvestor demo deadline · week 10

Live with real users · week 8

Two weeks ahead of the investor demo deadline.

8 weeks

from kickoff to live MVP with real users — two weeks ahead of the investor demo deadline

40%

of support queries deflected by the AI assistant during the pilot, reducing support load immediately

Funded

founders secured their next round using live traction data from the MVP we shipped together

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