BOOSTIA AI / AI CONSULTANCY & ENGINEERING AGENCY

The right AI.The right tools.Real business impact.

Voice agents, knowledge assistants and agentic automation, connected to the way your company works. We scope, build and orchestrate agentic systems in production, for startups, scale-ups and SMEs in France and Switzerland.

  • RAG
  • MCP
  • AGENTS
  • EVALS
  • REVIEW
VOICE AI / CALLEATILLUSTRATIVE FLOW

A conversation.
A structured next step.

Customer: “Can I order two pizzas for 7:30 pm?”
Agent: context and availability prepared. Source: restaurant menu
A person confirms the details before the next action.
Knowledge / RAGCalendar / APICRM / MCP

The challenge

Launching an AI agent is easy. Keeping it running in production is not.

Most teams have a pilot, a clever prompt, one more tool. Nothing connects them, nobody measures them, and the day the agent gets something wrong, nobody knows why.

Boostia is an AI consultancy and agency: we don’t sell a tool, we build the system that makes your agents reliable, useful and auditable, and we hand it over to your team.

Without a harness

  • Isolated pilots that don’t talk to each other
  • No testing before going live
  • Made-up answers, untraceable sources
  • No human oversight
  • Costs and errors discovered after the fact

With Boostia

  • An orchestrator that coordinates the agents
  • Evals and tests run before every deployment
  • RAG: sourced answers, and a refusal when the answer is not in the knowledge base
  • Human-in-the-loop on sensitive cases
  • Traceability of every decision and every call

What we do

Five areas of expertise, one goal: real business impact.

A focused engagement or an outsourced AI team, from strategy to deployment. Each capability is scoped and configured for your project.

01 · CALLEAT

Voice AI

Turn calls into structured next steps: orders, appointments, lead qualification, with human handoff. Our flagship solution: Calleat.

02 · RAG

Knowledge AI

Put your documents to work: knowledge assistants, sourced answers, a refusal when the context is missing.

03 · MCP

Agentic Automation

Connect your tools and workflows: autonomous agents and multi-agent systems, CRM & API, scoped permissions, approval steps.

04

AI Consulting

Find a practical starting point: use-case discovery, prioritisation by value, feasibility and risk, fractional Chief AI Officer.

05 · EVALS

Custom AI Development

Build around your business: custom applications, tests and guardrails, documentation and handover.

06

Team enablement

Practical training for your teams: you stay in control of the systems we build, with tests delivered alongside the code.

Our signature · Agentic Automation

A harness, not a pile of tools.

A central orchestrator coordinates specialised agents. Every exchange is put in context, evaluated and traced. Hover over or select an agent.

Orchestrator and specialised agents ORCHESTRATORcontext · memory Voice · Calleatcalls & orders RAG · Knowledgesourced answers CRM · Salesqualification, follow-ups Quotes · Back officedocuments, invoices Support · Ticketstriage and replies Guardrails · Evalstests, human oversight

Selected agent

Orchestrator

It receives every request, picks the right agent, gives it the right context and tools, then checks the result before replying.

Our flagship solution · Calleat

Calleat: an agent from our harness, already at work.

A voice agent for restaurants: it handles incoming calls, understands the order in natural language, restates it, confirms it and passes it on. Built for a restaurant in Annecy. Proof that our approach works with real customers, on the phone, in the middle of service.

Incoming call · Annecy00:00
ORDER#—
  • Waiting for a call…
Pickup—
StatusPending

Simulated demo for illustration only: dishes, first names and opening hours are fictitious. It shows the real sequence: answer → understand → confirm → pass on.

Live RAG

Ask Calleat’s knowledge base a question.

Try a question a customer might ask. The answer comes only from the restaurant’s documents (fictitious here) and gives its source. A question outside the knowledge base gets an honest refusal, not an invented answer. Demo: local lexical search (BM25); in production: vector RAG (pgvector) and an LLM.

The answer will appear here with its source and relevance score.

01

Incoming calls, real-time voice

Picks up on every ring, even in the middle of service, and talks naturally with low latency.

02

Structured orders

Items, quantities, options, pickup time: restated, confirmed, then passed on without retyping.

03

Knowledge base (RAG)

Menu, opening hours, allergens, pickup: the agent answers from your documents and cites its source. Outside its scope, it does not guess.

04

Human-in-the-loop

Allergy, group booking, dispute: the agent transfers the call or schedules a callback. Every call is traced and transcribed.

Why now · voice AI news, October 2026

Voice agents have moved from gadget to infrastructure.

What is changing this year, with the sources so you can check for yourself.

17.97bn USD

A conversational AI market worth 17.97 bn USD in 2026

Forecast: 82.46 bn USD in 2034, around 21% annual growth (Fortune Business Insights, cited in the 2026 Famulor report).

Source: Famulor (FR) ↗
11bn USD

ElevenLabs raises 500m USD and reaches an 11 bn USD valuation

Series D round led by Sequoia (February 2026): synthetic voice becomes a core layer of enterprise AI.

Source: VKTR ↗
48%

Nearly half of deployments are used for booking or scheduling

Bookings, confirmations, follow-ups: appointment management is the number one use of voice agents, far ahead of spectacular demos (Famulor, 2026).

Source: Famulor (FR) ↗
72%

Voice quality is the main barrier, ahead of cost

72% of decision-makers cite voice quality and fluency as the No. 1 obstacle, compared with 38% for price (Voices.com 2026, via Famulor). That is where the difference between a robot and an agent is decided.

Source: Famulor (FR) ↗

Who it’s for

Startup, restaurant or SME: an entry point for everyone.

Fundraising is picking up again and AI is attracting most of the capital. Teams that raise funds have to deliver fast, with few people. That is exactly what Boostia offers.

114€m raised by French startups in one week at the end of September, including €90m in AIMaddyness (FR) ↗
82% of French Tech funding from 14 to 18 September went to AIFrenchWeb (FR) ↗
1.25bn CHF invested in Swiss startups in H1 2026, 123 deals, with Vaud in the leadStartupticker (FR) ↗

Startups and scale-ups · France & Switzerland

AI support: fractional Chief AI Officer

You have just raised funds and are hiring, but a full-time CAIO is premature. Boostia frames your AI strategy, steers your first agents and sets up the guardrails (human oversight, security, compliance).

  • Agentic AI roadmap in 2 weeks
  • Architecture: multi-agent orchestration, RAG (pgvector), MCP
  • Guardrails: evals, human-in-the-loop, OWASP/MITRE
  • Team enablement
Discuss an engagement →

SMEs, services, sales teams

Custom voice agent

Lead qualification in under 5 minutes, appointment booking, first-line support, follow-ups. The agent integrates with your CRM, your calendar and your automation tools.

  • Inbound and outbound calls within clear limits
  • RAG on your business documents, sourced answers
  • CRM, calendar, n8n, MCP and API connections
  • Transcripts, evals and dashboard
Scope my use case →

Our work

Agents that work in real businesses.

Restaurants & catering · Annecy

Calleat

Voice agent that processes orders on incoming calls, with RAG on the menu and human handoff.

Renovation

Quote generator

An application that prepares quotes for a renovation company, with an autonomous agent that picks up the project context.

Scooter garage

AppHelpdesk

Job management with payment, plus an autonomous agent that sorts tickets and uses RAG to answer those already covered by the knowledge base.

Field technicians

Job file

Quotes, invoices, job sheets, photos and history in one place. In the event of damage or an unpaid invoice, an agentic workflow prepares the file for the insurer.

Under the hood

  • RAG · pgvector
  • Agentic AI
  • Multi-agent
  • MCP
  • Real-time voice
  • Human-in-the-loop
  • Guardrails
  • Evals
  • n8n
  • Docker · Ollama
  • Claude API
  • Call traceability

Method

From idea to agent in production, in four steps.

Each step produces something you can see, test and measure.

  1. 01

    Scope

    Assessment, prioritised use cases, quantified success criteria before a line of code is written.

  2. 02

    Prototype

    A first agent connected to your real data, tested with your teams within a few weeks.

  3. 03

    Harden

    Evals, tests, guardrails, human oversight: the agent must fail visibly and survive mistakes.

  4. 04

    Deploy & run

    Go-live, observability, handover to your team, continuous improvement.

Trust

Agents you can put in front of a customer, an auditor or a recruiter.

Human handoff & approval steps

Sensitive cases (allergy, dispute, large amount) go to a person, with approval before any significant external action. The agent knows what it does not know.

Evals before every production release

Test suites, out-of-scope refusals, mutation tests: we hold our own deliverables to the rigour we sell.

Scoped permissions · AI Act & GDPR

Each agent acts only within the agreed scope. Transparency about the use of AI, data minimisation, rights of access and deletion.

Traceability

Transcripts, decisions and tool calls are logged. When something happens, you can explain why.

A practical starting point in 30 minutes.

Free assessment, no obligation: you leave with 3 prioritised use cases, whether or not you work with us.

Book my assessment →

Get in touch

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  • Only the information needed to reply to you.
  • No resale of data.
  • No decision made by an AI alone.
  • Access, correction or deletion on request.
Or book a time slot directly ↗ Or calculate my automation score (3 min, in French) ↗
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