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AI engineering studio

Bitit AI plans, builds and runs AI agents, LLM products and the software around them — for teams who need results this quarter, not a slide deck next year.

Start a project See what we do
Systems shipped
120+
Median to first release
9 days
Client retention
96%
Plan · Build · Measure
More than automation.Systems that decide, act and report back.
FintechHealthcareRetailLogisticsManufacturingEnergyPublic SectorSaaSInsuranceLegalFintechHealthcareRetailLogisticsManufacturingEnergyPublic SectorSaaSInsuranceLegal
What we do

One team from first idea to production.

Four practices, one group of senior engineers and designers. The people who scope your project are the people who deliver it.

01

AI & Agents

Agents, retrieval systems, and models that sit inside real workflows and are measured against real numbers.

  • Single & multi-agent systems
  • RAG, tool use & evaluation
  • Model fine-tuning
Explore practice
02

Product & Design

We test the idea with users and prototypes first, so the budget goes into the version worth building.

  • Discovery sprints
  • Interface & design systems
  • Zero-to-one product builds
Explore practice
03

Engineering

Fast, well-tested web and mobile software that your own engineers will be glad to inherit.

  • Next.js & React platforms
  • iOS, Android & cross-platform
  • Automated QA
Explore practice
04

Security, Data & Scale

Hardening, data pipelines, and architecture reviews that keep AI systems safe once real traffic arrives.

  • Security & compliance
  • Architecture audits
  • Data engineering & BI
Explore practice
Delivery loop

Agents do the heavy lifting. Engineers stay in control.

Every project runs on the same loop: automated where it is safe, reviewed by a person wherever it counts.

A simulated Bitit AI build session that cycles through example projects, showing each stage of the delivery loop.

Live simulation
Objective
$ bitit run "Triage inbound support tickets"
  1. 01

    Brief

    You set the goal, the number it should move, and the lines it must not cross.

  2. 02

    Break down

    Agents draft tasks, risks and acceptance tests; an engineer approves the plan.

  3. 03

    Build

    Sandboxed agents write and refactor code while senior engineers review every diff.

  4. 04

    Verify

    Unit tests, integration tests and model evals run on every change.

  5. 05

    Release

    Shipped with monitoring, rollback and a complete change history you own.

Example workflow
Selected work

Results you can measure.

We agree the target metric before we start — revenue, hours saved, risk reduced or cost removed — and report against it.

All case studies
Healthcare
62%
Calls deflected
3 min
Avg. booking time

Clinic Intake Assistant

A privacy-first assistant that handles appointment requests, symptom pre-screening, and FAQs for a multi-site clinic group.

Read the case study
Finance
<80ms
p99 latency
31%
Fewer false positives

Real-Time Fraud Scoring

Streaming transaction scoring with reason codes that analysts can read, for a digital lender.

Read the case study
Enterprise
11k
Tasks / month
4.2x
Throughput

Back-Office Agent Suite

Supervised agents that process invoices, onboarding packets and access requests with a reviewable trail for every step.

Read the case study
How we work

From first workshop to live system.

A delivery model shaped for AI projects: firm commitments at each stage and working code within the first week.

  1. 01Week 0–1

    Assess

    We sit with your teams, map the workflow and the data behind it, and rank the AI use cases worth pursuing.

    Clear priorities

  2. 02Week 1–3

    Prototype

    A working prototype with success metrics, test sets and safety rules agreed before full build begins.

    Measured from the start

  3. 03Week 3+

    Deliver

    Small senior teams using AI-assisted tooling ship production code in short, visible increments.

    Code in week one

  4. 04Ongoing

    Operate

    Monitoring, evaluation and regular tuning so the system gets better — and stays compliant — after launch.

    Partners after launch

Principles

What we stand for.

  1. 01

    Designed for AI from the start

    Models, retrieval and evaluation are part of the architecture, not a feature added in the last sprint.

  2. 02

    Senior people, start to finish

    The engineers in the kickoff call are the engineers writing your code. No hand-offs to a hidden bench.

  3. 03

    Measured by your numbers

    Each engagement is tied to a business metric we report on openly — not hours logged.

  4. 04

    Yours to keep

    Readable code, test suites, documentation and runbooks, so your team can take over whenever you choose.

Client voices

Relied on when the stakes are high.

  • “
    They challenged our original scope, and they were right. The assistant we launched was smaller than we planned and far more useful.
    Director of Patient Services
    Multi-site Clinic Group
  • “
    The model explains its decisions in language our analysts trust. That was the part every previous vendor struggled with.
    Head of Risk
    Digital Lender
  • “
    Every agent action is logged and reviewable. Our auditors signed off faster than on the manual process it replaced.
    VP, Business Operations
    Enterprise Software Company
  • “
    Code landed in the first week and we could watch progress daily. It felt like an extension of our own team.
    CTO
    Series B Logistics Startup
Start here

Tell us what you want to change.

Bring the problem, the number it should move and your deadline. We will come back with a plan within a week.

Start a project contact@bitit-ai.win