Science iterates. We accelerate.

Turn your data into decisions

Domain expertise that brings you the power of AI for your R&D and production.

Tools that harness your data, your documents and the scientific literature
to support your team, built from hands-on experience in alternative proteins
and aquaculture, so they answer the questions that matter.

Deep answers
from plain questions.

The answer to your next question often already exists, scattered across spreadsheets, databases, trial reports and papers. What is missing is the time to connect it.

At iterAI, we start from domain expertise: knowing, from trials and production, which questions matter. That is why we bring you AI tools that multiply your team's capacity.

Together, expertise and tools follow the path research already takes. It starts when you ask a question. Our tools connect your data, your documents and the literature, check them so every figure can be trusted, and model what the data can support. Your team always stays in the loop: they decide, backed by evidence, and iterate into the next round, independently once the tools are in place.

01

Literature
monitoring

Regular scans of the scientific literature on the topics you choose, and structured research briefs that tell your team what is new, what is solid and what it means for your process.

02

Reliable data

Years of trial logbooks and production records, in old and new formats, brought into one clean database. Every error is flagged for correction, never silently deleted.

03

Answers you
can defend

Analysis that shows where each result comes from, states its level of confidence, and says so when the data cannot answer a question.

04

Decision tools,
built with you

Production dashboards and early warnings, experiment reports and trial design, built on the same foundation around the decisions your team makes every day.

05

Strategic R&D
consulting

Domain expertise applied to your R&D and production questions: where your data can help, which questions to prioritise, and how to get there step by step.

06

Grant applications

We co-write EU and national funding applications with partners, contributing the experimental design, the literature review and the technical sections of the proposal.

01
Discovery call

We start from your questions: a conversation about your R&D and production, then a scoping session with your R&D and IT teams on what data you have and where it lives.

02
See it in action

The tools working on a representative dataset built for your sector, before any of your data is involved.

03
Co-create the roadmap

Together with your team, we agree the questions, the data and the tools the pilot will cover, prioritised by feasibility and return.

04
Pilot on a data sample

A short, fixed-scope, fixed-price pilot on an extract of your real data: read-only, nothing installed. We map your data with your team and agree the questions and assumptions together at every checkpoint.

05
Implementation

If you continue: deployment into your infrastructure, tools your team connects to directly, and hands-on training so your people run them independently.

Built with your team from start to finish.
You keep every deliverable, at every stage.

Three connectors, linked by
the Nucleus, for faster R&D
and more efficient production.

Your team's knowledge lives in three places: your production data, your internal documents and the scientific literature. Our connectors give the AI of your choice direct, controlled access to all three, through the Nucleus, our method: it holds a model of your process, built with your team around the questions they actually ask, and that model is yours to keep. Ask one question and the AI cross-checks your data against your documents and the published evidence, bringing back one clear answer. What it surfaces serves the production floor as much as the lab, from one-off answers to production dashboards.

They are built and maintained by iterAI, and each client runs on a separate instance.

AI of your choice Your team’s questions iterAINucleus SQLDATA DOCUMENTRETRIEVAL SCIENTIFICLITERATURE
Connector 01 · SQL data

Talk to your production data. No scripting required.

Your production and trial databases, queried in plain language. The AI reads the structure of your data, writes the query, runs it and returns tables, aggregations and charts.

  • Plain-language questions, translated into SQL and executed for you
  • Aggregations, comparisons and charts on demand, without waiting on a data analyst
  • Read-only by design: only queries that read data can run; any attempt to write is rejected at the connection
  • A shared notebook of data observations, so known quirks and caveats in your data are remembered from one session to the next
Questions you can ask

"Compare feed conversion ratio across rearing lines over the last quarter."

"Which photobioreactor runs fell below target productivity in May, and what did they have in common?"

"Plot larval weight at harvest against substrate moisture for this year."

Connector 02 · Document retrieval (RAG)

Search all your team's documents. And the papers in your own library.

Protocols, reports, trial notes, presentations and the papers in your library, indexed so the AI finds the right passage by meaning, not just by keyword, and tells you which file it came from.

  • Semantic search across your whole document base, including files nobody remembers writing
  • Connects to where your files live: local and network folders, Google Drive, Dropbox and GitHub
  • Every result points back to its source file, so answers can be checked
  • Periodic re-indexing keeps the search current as new documents arrive
Questions you can ask

"What did we conclude from the last fermentation scale-up, and which report has the details?"

"Find every protocol that mentions egg incubation temperature."

"Which production and processing conditions match the benefits we saw in the last feed trials?"

Connector 03 · Scientific literature

The whole field, on demand. Not just the first page of results.

Searches the major scientific databases in parallel, removes duplicates, filters for relevance and retrieves full text where it is openly available, so your team reads the evidence instead of hunting for it.

  • More than 250 million papers, searched in a single query
  • Deduplication and filtering, so each paper appears once
  • Full-text retrieval where open access allows, not only abstracts
  • Structured research briefs on request: key findings, methods and relevance to your questions
Questions you can ask

"What is the recent evidence on black soldier fly larvae reared on brewery by-products?"

"Summarise studies replacing fishmeal with insect meal in shrimp diets."

"Find papers from the last two years on omega-3 content in microalgae grown on side streams."

Built to be trustedDiscover why↓

Read-only: nothing installed

The tools read your data but never change it, and nothing is installed on your systems during a pilot.

Every answer: traceable

Each answer shows the query, document or paper it comes from, so anyone on your team can check it.

Your data: never shared

Never used to train AI models, kept separate from every other client, and deleted after the pilot. After deployment, your data stays with you.

Supporting the future of food and feed.

Insects
Insect farming

Our deepest domain. Black soldier fly first, plus house fly, mealworm and other commercially reared insects: rearing, nutrition, production data and the R&D behind them, understood from the inside.

Microalgae
Microalgae

Cultivation and process R&D, whether extensive in open raceway ponds or intensive in closed photobioreactors and fermenters: productivity, biomass composition (including lipid and omega-3 content) and cost of goods, with the data and literature to back every decision.

SCP
Other single cell proteins

Bacterial, yeast and fungal proteins grown by fermentation on sugars, gases or agri-food side streams: literature monitoring on strains, substrates and conditions, experimental design for trials, and analysis of fermentation data from lab scale to scale-up.

Aquaculture
Aquaculture

From salmon and shrimp to feed trials: fish and shellfish physiology, farm data and feed formulation with alternative proteins, backed by both theoretical training and hands-on formulation work.

Science-first.
Not hype-first.

Most AI services start from the technology: a language model connected to your folders. At iterAI, we start from your process, with a real understanding of the biology and the production systems behind your data, so our tools hold up in operations, not just in a demo.

The hard part of any data project is knowing what the data means: which variables drive yield, which measurements are unreliable, which differences between R&D and production are expected. A general assistant gives plausible answers. A plausible answer that is wrong costs more than no answer.

Every project brings together three things that rarely meet: biology and production know-how, statistical rigour, and the engineering to build tools on your data. The person who scopes your project is the one who does the work.

Rigour, honesty and a long-term perspective. Not just a tool, but a partner for the whole journey, which is exactly what iter means in Latin.

7+ years in alt-protein
We bring a deep knowledge of the sector, from research to production.
Rigorous data science
We develop and apply advanced statistics, not spreadsheet guesswork.
Hands-on experience
From dry and wet labs to industrial settings, across a wide range of species.
International network
We connect your scientific projects with the right partners.
Science communication
Trained to translate complex science for a broad audience.
Francesco Boatta, PhD, Founder of iterAI
Founder

Francesco Boatta, PhD

Science, data, sustainability.

Francesco has spent over a decade at some of the most exciting intersections of life sciences, data and industrial reality, in international teams. His background spans several disciplines, and he moves comfortably across them, surfacing the value that travels in between. This venture started from a simple conviction: AI capabilities need to be coupled with deep domain knowledge and critical expertise in order to bring tangible value to customers.

That's exactly what iterAI is built for: combining AI tools, scientific rigour and operational flexibility to address complex industrial problems. Without the hype.

Let's tackle the 21st-century nutritional challenge together.

What would you ask
your data?

Whether you're exploring a first pilot or scaling an existing initiative,
we'd love to hear about your challenge.