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AI Transformation · Security · Infrastructure

From the foundation to the whole company running on AI.

We build the foundation (models, data, infrastructure, in the cloud or on your own floor), put agents to work in every area, build the internal products that were missing, and run all of it from a single control tower. On top of thirty years of mission-critical operations.

Free AI assessment See the full journey

  • 0+

    Projects delivered

    Brazil and abroad

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    Assets managed

    On-premise and cloud

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    Countries served

    4 continents

  • 0 years

    On the road

    Mission critical

The market in 2026

Adopting is now consensus.
Operating is not.

Where the market actually is, measured by a source that does not sell AI: the AI Index Report 2026 from Stanford HAI, now in its ninth edition.

  1. Organisations already using AI

    88%

  2. Use generative AI in at least one business function

    70%

  3. Agent deployment, across nearly every business function

    single digits

Adoption is nearly universal. Operating with agents has barely begun. That distance is exactly what the six layers cover.

Source: Stanford HAI · AI Index Report 2026

See the other figures

The stack

The level only rises if the layer
below it is full.

Layer 1 of 6

AI Foundation

Models, data and infrastructure: in the cloud, in the enterprise, or in-house.

The layer almost nobody builds properly, and the one that decides everything else. A single gateway for any model, routing by cost and capability, data classified and vectorised, and an honest choice between cloud, enterprise and on-premise based on what the data allows , not on what the trend demands.

  • Multi-model gateway: swap the LLM without rewriting the application
  • Routing by cost, latency and how critical the task is
  • Deploy in cloud, enterprise or local (sensitive data never leaves the perimeter)
  • Data pipeline, vectorisation and sensitivity classification
  • Claude
  • GPT
  • Gemini
  • Llama
  • Mistral
  • Bedrock
Layer 2 of 6

Your company intelligence, operated

What your company knows, available to whoever needs it, and to the agents too.

Documentation scattered everywhere, runbooks living in three people’s heads, decisions buried in email threads. We turn that into a living base: continuous ingestion, curation, per-area permissions and reliable retrieval. It is what makes an agent answer with your company context instead of the internet average.

  • Continuous ingestion of docs, wikis, tickets, code and meetings
  • RAG with evaluated retrieval, not merely plugged in
  • Permission by area: each person retrieves only what they may see
  • Curation and detection of stale or conflicting knowledge
  • RAG
  • Embeddings
  • pgvector
  • Reranking
  • Knowledge graph
  • Recall evaluation
Layer 3 of 6

Agents across the whole company

Support, IT, finance, sales, logistics, HR: each area with its own.

Agents that do not stop at chat: they execute. Connected to the ERP, the CRM, the service desk, IoT devices and internal APIs, with granular permissions, human approval where the risk demands it, and an audit trail on every step. One agent per process, not a generic chatbot for everything.

  • Native integration with ERP, CRM, ticketing and IoT
  • Tools with scoped, revocable permissions
  • Mandatory human-in-the-loop for high-impact actions
  • Multi-agent orchestration for processes that cross areas
  • Tool use
  • MCP
  • Orchestration
  • Human-in-the-loop
  • ERP
  • CRM
Layer 4 of 6

Micro SaaS and internal solutions

Software that never paid off before, and now does.

That internal system always stuck in the queue because the return never justified six months of a squad. With AI in the development cycle, it now fits in weeks. We build micro SaaS for your market and internal tools that solve the specific problem, without forcing the company to bend around an off-the-shelf product.

  • Micro SaaS aimed at your market, with your business rules
  • Internal tools that replace the critical spreadsheet and the fragile macro
  • Portals and copilots embedded in the systems the team already uses
  • AI-accelerated development cycle, with human review
  • Micro SaaS
  • Embedded copilots
  • APIs
  • Integrations
  • Process automation
Layer 5 of 6

Control tower

The entire business chain in a single view, for the board and the executive team.

The tower is not the technical team’s dashboard. It is where the board and the chiefs see the company link by link, from demand to cash, with each indicator operated by AI and flagging deviation before it becomes a problem. It works for any business because the chain is the same: retail, manufacturing, services and software change the names of the links, not the shape. The Command Center and the SOC are still there, now as what feeds this view.

  • An indicator per link of the chain, from acquisition cost to cash cycle
  • Consolidation for the board with the origin of every number visible
  • Deviation flagged by the agent, rather than a report to dig through
  • L1/L2 Command Center and 24x7 SOC, both model-assisted
  • Board view
  • Indicator per link
  • Command Center
  • 24x7 SOC
  • AI observability
  • SLA
Layer 6 of 6

Engineering and culture with AI

Your team running it alone, because a transformation that depends on a vendor is not a transformation.

The last layer decides whether the previous one survives. We move the practices inside your team: how to develop with AI without losing review, how to write the evaluation before the prompt, when to trust the agent and when to doubt it. The stated goal is for you to stop needing us for this.

  • AI-assisted development with a defined review standard
  • Evaluation practices: tests before prompts, metrics before deploys
  • AI usage policy and risk criteria by task type
  • Enablement by profile: engineering, operations, business and leadership
  • Enablement
  • AI code review
  • Evals
  • Usage policy
  • Training
  • Playbooks

Keep scrolling to fill the tank

The company brain

Data in a silo answers a team.
Related data answers the business.

Every question a director actually asks cuts across three or four systems that never spoke to each other. That is why the answer took a week and arrived stale. Once the whole company’s knowledge becomes a single mesh, with per-area permissions preserved, that same question is answerable in minutes, with the evidence attached. That is where AI touches revenue: not by writing better, but by shortening the distance between noticing and deciding.

An illustration of a company knowledge mesh: 10 data domains, each with its own cloud of records, and 12 lit bridges connecting different domains. Each bridge represents a business question that can only be answered once two systems that never spoke to each other start to.

ERP and finance Sales and CRM E-commerce Logistics Service desk Infrastructure Security People Marketing Documentation
  • What the company already has records, documents and logs, each in its own system
  • What only exists once related the business question that dies inside a silo

The mesh is neutral and only the bridges are lit, and that is the argument of the figure: the value is not in having the data, it is in the data touching. This illustrates the domain structure almost every company has; it is not a screenshot of a client. The placement of each node is arrangement, not measurement.

One mesh, six readings

Each seat asks a different thing, and each answer crosses different domains. It is the same base: what changes is the slice and the permission.

  • Board

    Board

    Is the plan holding, and what changed since the last meeting?

    • ERP and finance
    • Sales and CRM
    • Marketing
    before
    a pack closed the month before
    with the mesh
    the same reading, on yesterday’s data

  • CEO

    CEO

    Why did margin drop, and exactly where?

    • ERP and finance
    • Logistics
    • Sales and CRM
    before
    three teams extracting, a week to reconcile
    with the mesh
    the breakdown on screen, with the number behind it

  • COO

    Operations director

    Where does the order stall before it ships?

    • E-commerce
    • Logistics
    • Service desk
    before
    a spreadsheet per carrier, refreshed on Monday
    with the mesh
    the bottleneck flagged while it is happening

  • CFO

    Finance director

    What does it really cost to serve each customer?

    • ERP and finance
    • Logistics
    • Service desk
    before
    averaged allocation, which hides the customer who loses money
    with the mesh
    cost per customer, with freight, returns and tickets inside

  • CTO

    Technology director

    Which change caused this incident?

    • Service desk
    • Infrastructure
    • Documentation
    before
    correlation held in the memory of whoever was on call
    with the mesh
    the deploy window and the ticket linked automatically

  • CISO

    Security director

    Should this access still exist?

    • Security
    • People
    • Infrastructure
    before
    a quarterly review, done from an export
    with the mesh
    the gap between headcount and permissions, flagged the same day

The loop, and where the person comes in

The machine reads, relates and proposes. A person still decides, and what was decided returns to the mesh. Full autonomy exists only where it was explicitly delegated, and never for sensitive, financial or access actions.

  1. 01

    Relates

    The mesh links system data, documents and conversations, with per-area permissions preserved.

  2. 02

    Observes

    Specialist agents read the mesh continuously and compare it against what was expected.

  3. 03

    Proposes

    Whatever departs from the expected becomes a hypothesis with evidence attached, not a bare alert.

  4. 04

    Decides

    A person approves, adjusts or refuses. Sensitive, financial or access actions are never automatic.

    human
  5. 05

    Executes

    What was approved runs in the systems of record, with every step logged.

  6. 06

    Learns

    The outcome returns to the mesh, and the next proposal is born with that feedback inside.

10 domains, 12 bridges drawn, and per-area permissions that survive the trip: whoever could not see the data in the system of record still cannot see it in the answer.

See how the mesh is built

Control tower

The board does not need a report.
It needs the chain, live.

The tower is not the technical team’s dashboard. It is where the board and the executive team see the whole company, link by link, with each indicator operated by AI and flagging deviation before it becomes a problem. The Command Center and the SOC still exist, and they are now what feeds this view rather than what it is.

What reaches the board

The chain, link by link

  1. 01

    Demand

    attract and qualify whoever can buy

    • Acquisition cost CMO AI-operated
    • Lead quality CMO AI-operated

    also called marketing, lead generation, traffic

  2. 02

    Sales

    turn interest into an order, within margin

    • Stage conversion CRO AI-operated
    • Quote margin CFO Hybrid

    also called commercial, funnel, e-commerce, tenders

  3. 03

    Supply

    have something to sell, without money parked in stock

    • Stockout and overstock COO AI-operated
    • Replenishment cost CFO Hybrid

    also called purchasing, contracts, capacity, licences

  4. 04

    Operations

    produce or execute what was sold

    • Availability CTO AI-operated
    • Rework COO Hybrid

    also called factory, project, platform, service

  5. 05

    Delivery

    get it to the customer on the agreed date

    • On-time delivery COO AI-operated
    • Cost per delivery CFO AI-operated

    also called logistics, rollout, go-live, provisioning

  6. 06

    After-sales

    keep the customer, and find out early when they are leaving

    • Churn risk CRO AI-operated
    • Cost to serve CFO Hybrid

    also called support, customer success, warranty, renewal

  7. 07

    Finance

    close the books and say whether the month worked

    • Margin by line CFO Hybrid
    • Cash cycle CFO Human

    also called controlling, billing, collections

Who operates each number

  • AI-operated the agent computes it, compares it against expectation and flags the deviation. A person looks at what was flagged, not at the whole spreadsheet.
  • Hybrid AI prepares and proposes, a person confirms. This is where the numbers that depend on judgement or on a contract live.
  • Human still decided by people, with AI only organising the material. Nobody automates what answers to the auditor.

The chain above is deliberately generic, and that is what lets the same tower serve different businesses: retail, manufacturing, services and software change the names of the links, not the shape. Of the 14 indicators drawn, 8 are operated end to end by AI, and the rest still go through people.

See inside the tower

Cost per model

There is no best model.
There is the right one for the task.

Between the most and least expensive model on this list there is more than a hundredfold difference in cost per token, and a good share of a company’s work does not need the top. We operate both ends: each task is routed to the tier it actually requires, and the frontier is kept for what genuinely depends on it. That is where the bill comes down, and not from a vendor discount.

Cost, capability and objective

22 models in operation, 11 of them run inside your walls

125× between the expensive end and the cheap one

A map of twenty-two language models, with cost per million tokens on the horizontal axis, on a logarithmic scale, and four capability tiers on the vertical axis: volume and latency, work at scale, the workhorse, and frontier. Each tier states what it is for. Colour separates three licences: proprietary API-only, open-weight with a restrictive licence, and open source with a permissive one.

Volume and latency cost per call is what decides Work at scale has to think, but not much The workhorse where almost every production agent lives Frontier long reasoning and hard decisions US$ 0,1 US$ 0,3 US$ 1 US$ 3 US$ 10 cost per million tokens · logarithmic scale more expensive → capability Phi-4 Gemma 3 Gemini Flash-Lite GPT-5 nano Mistral Small Claude Haiku Llama 4 Scout Gemini 2.5 Flash Llama 3.3 70B DeepSeek V3 Qwen 2.5 72B GPT-5 mini Llama 4 Maverick DeepSeek R1 Command R+ Mistral Large Claude Sonnet Grok Gemini 2.5 Pro GPT-5 o3 Claude Opus

Colour is the licence, and it decides whether a model can enter your datacenter: proprietary exists only through an API; open-weight downloads and runs, but the licence restricts commercial use; open source is Apache 2.0 or MIT and runs anywhere. The horizontal axis is order of magnitude from public list prices, not a price table: price changes often, varies by region, contract and volume, and drops with every generation. The vertical axis is an ordered tier, not a benchmark score: between neighbouring models the ranking shifts with the benchmark, but the tier holds, and it is the tier that decides a project. Height within a tier is only arrangement so the names do not stack. A new model ships every week; the shape of the map is what does not change.

The multi-model gateway does this routing in production, and switching models later is configuration rather than a rewrite.

See where each model runs

A one-minute diagnosis

Most companies sit between
L1 and L2. Where is yours?

AIMI measures maturity across six pillars and tells you where you are, how far you can get, and what that is worth in savings. No sign-up, no email, no waiting.

Calculate my company index
  1. L1 Fragmented AI
    most companies
  2. L2 Controlled AI
    most companies
  3. L3 Integrated AI
  4. L4 Optimised AI
    where AI pays for itself

Why haapit

Everyone sells AI.
Few know how to keep it running.

An AI consultancy hands over the model and leaves. We stay: we are the same people who monitor the environment, answer the incident at three in the morning and sign the SLA. That is why our AI is designed for the day it fails, not only for the day of the demo.

  • Obsessed with the client

    Our work is measured by the satisfaction of the person on the other side, end to end, with numbers rather than assumptions.

  • Pure excellence

    Every process we build is monitored and measured. What is not measured does not improve, and what does not improve we do not ship.

  • Plugged into the business

    We understand the detail of the client business before proposing technology. IT that does not move the business needle is cost, not investment.

In adoption at haapit

AI governance stopped being a slide.
It became a certifiable standard.

ISO/IEC 42001 is the first international standard for an artificial intelligence management system: auditable, certifiable, and in the same structure as the ISO 27001 your company may already hold. We are in study, adoption and internal validation before taking it to a client, exactly as we did with PCI-DSS.

Read the full paper See the certification practice
  • 2023 Published by the ISO/IEC JTC 1/SC 42 committee
  • 38 Annex A controls, across nine objectives
  • 36% Of companies already cite it as a reference, per the AI Index 2026
  • 4 to 10 The mandatory clauses, in the same structure as ISO 27001

In production every day

How we work

Four steps. No surprises.

  1. Diagnosis

    We get inside the environment, measure what is there and hand back an honest map: what works, what is bleeding money and what is genuine risk. No cost, no commitment.

  2. Design

    We prioritise by impact and feasibility, not by hype. You get scope, milestones, a success metric and the number that justifies the investment before signing.

  3. Delivery

    A dedicated team, short delivery cycles and visible progress. No project that disappears for three months and comes back different from what was agreed.

  4. Operation

    Support with an SLA, periodic reporting and continuous improvement. We transfer knowledge to your team. The goal is not for you to depend on us forever.

In numbers

What changes when the operation
stops fighting fires.

What left human hands

the agent does it a person does it

  • Time to resolve an incident MTTR
    −84%

    Triage and runbook executed by the agent before anyone is paged

  • Alerts that reach a human operational noise
    −82%

    Automatic deduplication and correlation with deploys and changes

  • Human effort on repetitive procedures manual effort
    −72%

    Runbook executed by the agent, with a person stepping in only on exceptions

  • Audit preparation effort certification
    −60%

    Evidence collected continuously, rather than assembled in audit week

  • Level 1 tickets service desk
    −54%

    Agents handling the repetitive work before it becomes a ticket

And what improved another way

  • Support NPS satisfaction
    +80%

    Availability and accuracy in the fix: solved first time, and solved fast

  • Monthly cloud spend AWS · Azure · GCP
    −41%

    Continuous rightsizing, reservation review and shutdown of idle capacity

And where the change is an order of magnitude

  • Root cause analysis

    25 to 30 min 60 seconds

    25 to 30× faster

    The agent correlates alerts, changes and logs across the layers involved before anyone opens a terminal

  • Wait time in support

    over 30 min under 1 min

    over 30× less waiting

    The problem is solved before it becomes a queue, and that is what lifts NPS

Ranges observed across our projects. The number for your environment comes out of the assessment.

See the cases

Ask for a free assessment of your environment :)

A technical assessment of the current environment, with a direct read: what AI can automate, what needs a foundation first, and what is not justified at this point.

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