Aiger Innovations
Our work

The work, and what it changes.

Three engagements, composed from the problems we’re asked to solve most often. Each one is the same pattern: a problem that costs the business hours every week, a foundation built securely underneath it, and a workflow that hands people back their time.

Illustrative, not client results. These are illustrative composites, not client case studies. Company profiles, figures and timings show the shape of the work; they are not results from a named client.

  1. The board pack that took five people a week.
  2. The analysts were the integration layer.
  3. The knowledge base that got shelved — and the one that didn’t.
Operating company · Illustrative engagement

The board pack that took five people a week.

A 600-person distribution business

The problem

Every month the finance team pulled numbers out of the ERP, the CRM and a dozen spreadsheets and pasted them into forty slides. Five people, most of a week — and the board still asked questions nobody could answer in the room.

What we built

  • Claude Enterprise for finance and leadership, with single sign-on and a usage policy
  • A staging area for the ERP, which doesn’t allow direct AI access, plus connectors into the CRM and shared drives
  • An ontology of customers, products, regions and cost centres, so every number has one definition
  • A board-pack agent that drafts each slide, re-checks every figure against its source and flags what changed since last quarter

What changed

MeasureBeforeAfter
Board packMost of a week, five peopleDrafted overnight, reviewed in a morning
Figures checkedSpot checksEvery figure, against its source
Questions in the room“We’ll come back to you”Answered from the same data, on the day
The board-pack agent at work: five steps across SharePoint, the CRM and mail, a folder it isn’t allowed into skipped, every figure checked, and the draft held for sign-off.
Investment firm · Illustrative engagement

The analysts were the integration layer.

A mid-market hedge fund with a twelve-person investment team

The problem

Analysts started at 06:00 copying market moves, broker research and yesterday’s fills into the morning note. Exposure questions waited days, because answering them meant joining positions to hundreds of call transcripts by hand.

What we built

  • Claude Enterprise for the investment team, with information barriers between public and private sides
  • Connectors into the portfolio and order systems, the research inbox and market data
  • An ontology of funds, positions, issuers and documents
  • A morning-note agent and an exposure-question workflow, every line cited to its source
Morning note — draft
Illustrative

Overnight, and what it means for the book

  • Asian semis sold off overnight on export-rule headlines; our two largest longs in the group opened lower in Tokyo.PXPMS
  • Broker A cut Issuer C to neutral on margin guidance; Broker B reiterated with a lower target.BRK-2BRK-3
  • Yesterday’s fills completed the Issuer F add; residual order working in the afternoon session.OMS
  • Issuer K reports after the close; consensus and our model diverge on gross margin.CALMDL
Awaiting PM edits9 sources citedmarket data · research · OMS · PMS
The morning note, drafted from market data, broker research, fills and positions before the desk arrives — every line cited to its source.

What changed

MeasureBeforeAfter
Morning noteBy hand from 06:00Drafted by 06:10, cited line by line
Exposure questionsDays, if someone had the afternoonMinutes, sized by position
ComplianceNo record of what AI sawEvery prompt and source on the record
Professional services firm · Illustrative engagement

The knowledge base that got shelved — and the one that didn’t.

A 300-person consultancy

The problem

An eight-month project poured every document into one search index. The results were noisy, nobody trusted them, and fee-earners went back to asking around. Proposals were rebuilt from scratch every time.

What we built

  • Structure first: a map of clients, engagements, sectors, people and deliverables
  • A decade of past work labelled — and checked by the people who did it
  • A proposal agent that starts from the most comparable past wins
  • Client confidentiality enforced: people only reach the work they were cleared for

Against a pile

Reads thousands of chunks. Most are near-misses; some contradict each other.

Against a structure

EngagementSectorProposalPartnerReportClientPerson

Reads four records. The path is the answer, and every step is traceable.

The same question against a pile of documents and against a structure. With structure, the AI reads a handful of records instead of thousands.

What changed

MeasureBeforeAfter
Finding precedentAsking around, for daysMinutes, with the lead named
First-draft proposalsEvenings, from scratchThe same day, from past wins
TrustNobody used the searchEvery answer traced to its source

Your problem won’t look exactly like these.

Bring the one that costs you the most. We’ll sketch the engagement with you in the first session.

Book a discovery call