Engineering by NSigma

AI implementation for fundamental investment teams

Your research process, engineered by a Forward‑Deployed Investor.

A Forward-Deployed Investor is a seasoned buy-side analyst who embeds with your research team, diagnoses where your process loses hours, and recommends what is worth building. NSigma engineers then build and maintain those workflows around the way your team invests.

Book 45 minutes See what we build

Perspective. An investor’s view of your process.

More judgment per analyst-hour.

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The implementation gap

Licenses are not capability.

Almost every fund now pays for AI. Very few have changed how research gets done.

Source: AIMA, 2025 survey of fund managers on generative AI use. The last step is not measured in the survey. It is the work we do.

The new due diligence question

“How does your team use AI within your style and mandate?”

The answer that worries them

“We pay for three AI subscriptions.”

The answer that reassures them

“We built AI into the process you invested in. It works inside our mandate, and every output is on the record.”

Allocators backed your process. Generic tools pull it toward everyone else's. That is how style drift starts.

Where the hours go

Upkeep fills the week. Judgment gets what is left.

The same filing can be either kind of work. Refreshing the model from it is maintenance. Reading it to test a new idea is judgment. The work is to separate the two steps inside each task, then automate the first.

0hAn analyst's week in earnings season54h

Work that should shrink Maintenance

  • Extracting disclosed KPIs
  • Reconciling them to the model
  • Re-keying into templates
  • Building earnings prep packs

Work that should grow Judgment

  • Interpreting what changed
  • The questions nobody had time for
  • One more name covered
  • Earlier warning on the thesis

Illustrative baseline from Thesis & Method's mock diagnostic. Not a client measurement and not a performance claim. The diagnostic measures your own split.

Two parts, one service

An investor defines the question. Engineering makes it work.

The Forward-Deployed Investor

An investor who learned to build, not an engineer who learned finance.

Every engagement is led by a CFA charterholder with 17 years of buy-side equity research, who has led AI implementation for a global equity team. Your FDI diagnoses your process, recommends what to build, and proves whether it works.

NSigma engineering

The engineering arm of the service.

About 30 developers, PhD-level quant and machine learning support, and ISO 27001-certified information security management. NSigma engineers build, test and maintain the agreed workflows.

Your team

Keeps every investment decision.

Analysts approve every proposed change. The PM owns the thesis, the sizing and the decision. No tool sizes positions, enters orders or publishes.

Meet the team

Show the work

A research record. A clear build order. A better question.

Three outputs, recreated with sample data so you can see their shape. In an engagement they are built on your names, your models and your note standard.

Thesis trackerIllustrative, sample data

Broadcom (AVGO)

Core long · Reviewed Oct 2026 · On track

Custom accelerators for three hyperscalers become a second growth engine next to networking.

What has to be true

AssumptionStatus
Accelerator ramp at three hyperscalersOn track
Networking share holds against rival EthernetWatch
Software margins hold after price changesOn track
Gross margin holds as custom mix risesWatch

FY27 AI revenue, $bn: house vs Street

JanAprJulOct

House 74 vs Street 71. The gap narrowed from 7% to 4%. That is the conversation for the next review.

Sell triggers

  • A top-three customer takes accelerator design in-house
  • AI revenue guide cut two quarters in a row

Sample record. Not a view on the company.

Diagnostic readoutIllustrative, sample data

Where the week goes

63%of analyst hours are maintenance
Holdings with no thesis update this year9
Prints last season with no note at all6

One print, step by step

Retyping figures40m
Transcript70m
Drafting60m

170 minutes from release to note. Sent 21:54, with no tie-out to the model.

Build order

First
Thesis tracker and post-print digest
Then
KPI history per name
Later
Public-data collection
Not yet
Card-data dataset (vendor rights not confirmed)

Before any build: data rights cleared, AI policy agreed, a named reviewer.

AI Bubble MonitorIllustrative, sample data

Separate signal from story.

Thematic observatory · 48 signals

27Hot, dangerous 6Hot, healthy 10Weak, deteriorating 5Calm, fine

Question for the analyst

Is installed capacity turning into demand at the rate the thesis requires?

Sample grid. Not current market observations.

Built for how you invest

Your style determines what we build first.

Prepare faster. Analyze deeper. Research further. The diagnostic decides where the next hour of engineering is worth spending.

StyleWhere the hours goTypical starting points
Earnings-driven long/shortPost-print notes, KPI tie-outs, sell-side triage, crowding checksPost-print digest with proposed model updates, thesis and estimate record, crowding check
Long-only quality and compoundersWatchlist upkeep, moat durability, capital allocation, management meeting prepThesis record with history, signpost monitors per holding, moat and pricing-power investigations
Value and contrarianFiling re-reads, earnings normalization, asset valuationCited filing search, normalized earnings and asset-value models, value-trap investigations
Event-driven and activistDeal and proxy review, ownership, regulatory diligence, catalyst timingEvidence room, ownership and voting maps, catalyst and regulatory trackers, scenario workbooks
ThematicSignposts across many companies, value chains, company exposureThematic monitors, exposure maps, theme-to-earnings scenarios
Small and mid capFragmented data, management and ownership, funding needsCompany evidence room and starter model, public-data collection

Multi-manager pods, global and emerging markets, quantamental and income strategies are covered in the diagnostic. The first build follows your style, not a template.

See examples of what we build

The engagement

Start with the process. Commit at each decision gate.

The diagnostic stands alone. A build is a separate decision, and so is everything after it.

PhaseWhat happensYour team's timePrice and decision gate
01DiagnosticThree weeks, 2 to 6 days on site by team size. A baseline, a working sample, a build order and a priced recommendation.About one day for the PM, half a day per analyst, plus a one-hour readout.Fixed fee, scaled to team size. Approve a build, change the scope, or stop with the report.
02BuildFirst workflow live in 2 weeks, the full build in 4 to 8. NSigma engineers build and test on your past quarters, with your FDI specifying and checking.A named reviewer, about 2 hours a week.Agreed cap. Your reviewer accepts the output before use.
03Training4 workshops and 2 clinics within 30 days. 15 live hours, at most 6 analysts per cohort.Attendance at the workshops and clinics.Priced in the proposal. Your team shows it can use and check the workflow.
04ManagedMaintenance, improvements and FDI time. 12-month initial term from agreed go-live.Priorities and exceptions on an agreed cadence.From $5,000 a month. Optional NSage earnings forecasts: $5,000 to $10,000 a month by coverage.

Diagnostic, build, training and a year of managed service usually cost less than hiring one analyst.

Read the scope, schedule and terms

For the COO and data lead

Your environment. Agreed controls.

  • Fund-hosted, managed or hybrid, chosen with your IT and your licensed-data permissions.
  • Named access only, separate client environments, ISO 27001-certified information security management.
  • Your positions, theses, models and notes never enter a shared component or another client's work.
  • A personal trading restriction for everyone with access to your data.
Read security and data

What we build · Configured to your process

Better questions. Working systems.

Choose the point where your research loses momentum. See how an engagement turns it into a repeatable workflow, and how we prove it works.

Get to the debate while it still matters.

A filing should enter your research process with its definitions, discrepancies and implications intact.

InputRelease, filing and house model
WorkflowExtract, reconcile, flag
ReviewAnalyst-approved update

Workflow schematic, configured during the engagement.

What we would build

A repeatable extraction and reconciliation pipeline, a queue of exceptions, and a draft research update linked to the source passages.

How we prove it

Tested on your own past quarters, using only data available at the time, before it goes live.

The deliverable

A reviewable update in the format your team already uses.

Connected by design

Every workflow should leave the firm smarter.

Every workflow reads from and writes to a small set of shared records, so each one makes the next cheaper to build. It runs where you choose: fund-hosted, managed or hybrid.

L

Thesis and estimate ledger

Theses, triggers and dated estimates, joined to outcomes. What you believed, what you expected, and which evidence changed the view.

E

Evidence bank

Cited search across years of your own notes, models and transcripts. Source passages, definitions, dates and unresolved contradictions.

G

Relationship graph

Customers, suppliers and competitors of every name, refreshed each filing cycle. The economic links through which a development could matter.

Case · All three limits in one question

Could cooling content protect Vertiv's growth if data-center builds slip?

The chain: a grid interconnect delay pushes a data-center go-live, fewer megawatts are installed this year, and the timing of Vertiv's cooling revenue moves.

Megawatts installedWatch: hyperscaler capex, utility interconnection queues
Liquid-cooled shareWatch: rack power density, GPU platform mix
Content per MWWatch: Vertiv and peer disclosures, order mix
Vertiv shareWatch: competitor wins, pricing
Cooling revenuePlus retrofit and service

Illustrative inputs, indexed to base case = 100. The dashed line marks the base case. The diagnostic replaces them with sourced figures. A proposed investigation, not a completed finding or a forecast.

Builds can slip 20% and cooling revenue still grow if liquid share and content per MW rise. The debate is which moves faster.

Scope follows the question

Engagement patterns, not a shelf of software.

We select and configure the work against your data rights, investment style and review requirements. Specialized forensic, event-driven and quant tools are kept for funds whose mandate calls for them, and the diagnostic decides. Where a workflow uses our own components, such as Research OS or NSage, they are licensed with the engagement and configured to your process.

The engagement · From diagnosis to daily use

Begin inside your process. Earn the right to build.

The engagement starts with your Forward-Deployed Investor embedded in your research process: observing how the work gets done, asking what the PM needs, measuring where the hours go, and workshopping what to build first. Each phase ends with a concrete output and a decision. Nothing proceeds without your approval.

01Diagnostic · three weeks

Find the constraint. Price the remedy.

Your Forward-Deployed Investor sits with the team and follows the work from the source document to the investment conversation. Interviews are checked against actual work samples and observed handoffs.

Week one, on site

  • Day one with the PM or CIO, including the team meeting
  • Half a day with each analyst
  • One day with operations, compliance and the data or vendor lead: standard at funds with 6 or more analysts, optional below that
  • Playback with the PM on the last day

Weeks two and three

  • Analysis of where the hours go
  • A working sample, built on one of your holdings
  • Build order and acceptance tests

End of week three

  • A one-hour readout with the PM
  • A priced recommendation: build, prepare first, train, or stop

On-site days by team size

PM + 2 analysts2 days
PM + 4 analysts3 to 4 days
PM + 6 analysts5 days
PM + 8 analysts6 days
PM or CIO dayTwo analysts a dayOperations and compliance dayOptional at smaller funds

Your team's time: about one day for the PM, half a day per analyst, one day for operations and compliance at larger funds, plus a one-hour readout.

What you receive

Where your time goes

Hours by task, maintenance vs research, and print-to-note time.

Your research backlog

The questions that could change a position, ranked by the PM.

A build order

First, then, later and not yet, each with the hours it gives back.

A working sample

The first build, run on one of your own holdings.

A build recommendation

Build, prepare first, train, or stop. With costs and acceptance tests.

Standalone engagement. Use the report with us, with another provider, or not at all. A fixed fee, scaled to your team and agreed before we start.

Decision gate. Approve a build, change the scope, or keep the diagnostic and stop.

Before any build

Four things are confirmed with you first. The diagnostic checks each one, so none is a surprise.

Data

Which sources you are licensed to process with AI.

Policy

An AI usage policy. If you don't have one, we help write it.

Owner

One person on your team who reviews outputs, about 2 hours a week.

Where it runs

Your environment or ours, agreed with your IT.

02Build · 4 to 8 weeks

Prove it on the work you already know.

NSigma engineers build to capped hours agreed before work starts, with your FDI specifying and checking the work. Each workflow is tested on your past quarters, using only data that was available at the time. The first workflow is live within two weeks; the full build order takes four to eight weeks, depending on what it includes.

  • Acceptance targets agreed per build before implementation
  • Example targets: 70 to 85% of proposed model lines accepted on a held-out quarter, and a draft within 60 minutes of the print
  • Unused hours are not billed; overruns within unchanged scope are absorbed by us
  • Weekly reporting of hours and forecast cost
  • Working code, documented source and calculation logic
  • Acceptance results, known limitations and a release plan

Decision gate. Your reviewer accepts the output before the workflow goes into use.

03Training · with your analysts

Your analysts leave able to build, not just use.

4

workshops, three hours each

2

clinics of 90 minutes, within 30 days

6

analysts at most, in one cohort

15

live hours in total

  • Build the data room and model for a new name
  • Turn a company question into an evidence plan
  • Build KPI dashboards and memos from their own data
  • Backtest an idea using only data available at the time
  • Spot when an output is wrong, and say why
  • Use Claude Code and Codex day to day on their own names

Decision gate. The team shows it can use and check the workflow.

04Managed · ongoing

Research changes. The system has to follow.

New disclosures, revised definitions and changing priorities all create maintenance work. Managed support keeps it working, keeps improving it, and keeps FDI time on your calendar.

  • Failures and unresolved exceptions
  • Analyst edits and recurring corrections
  • Data and model changes
  • The next useful improvement

Decision gate. Ongoing support is scoped separately around what is live. From $5,000 a month, 12-month initial term from agreed go-live.

Commercials

Priced to sit next to a hire, not a consultancy.

PhasePrice
DiagnosticFixed fee, scaled to team size and agreed before we start
BuildCapped hours, agreed in the proposal before work starts
TrainingProgram fee in the proposal; clinics included
Managed servicesFrom $5,000 a month; 12-month initial term
NSage add-onMachine-learning earnings forecasts on your coverage, $5,000 to $10,000 a month

Diagnostic, build, training and a year of managed service usually cost less than hiring one analyst.

Diagnostic50% at booking, 50% on delivery.
BuildBilled monthly up to the cap, with weekly reporting of hours and forecast cost.
Training50% at scheduling, 50% after workshop four.
Recurring feesMonthly in advance.
Scope changesWritten approval of cost and timing first.
Third-party costsHosting, model usage and data costs are identified in the proposal.

Questions a skeptical PM asks

Asked and answered.

Why not just give the analysts Claude?

They already have it. The gap is set-up: your KPI definitions, your models, your note standard and what counts as material. That is investment work first.

Who will we actually work with?

A Forward-Deployed Investor leads the diagnostic in person and stays on the engagement. Named NSigma engineers build and maintain the workflows. The proposal names everyone who will have access.

How much of our time does the diagnostic take?

One day with the PM or CIO, half a day with each analyst, one day with operations and compliance at larger funds, and a one-hour readout.

Does our data leave the building?

Only if you choose. Fund-hosted, managed or hybrid, set by your data permissions and agreed before release.

Do you work with funds that own the same names?

Yes, behind a strict information barrier: separate environments, no client data in any shared component, and a personal trading restriction. See security and data.

What if an output is wrong?

It carries its sources, so the analyst can check it. Overrides are logged, and every workflow is tested on your past quarters first.

What if the diagnostic says don't build?

Then we say so. You keep the plan and the baseline, and can use them with any provider or later.

How do we measure success?

Each build ships with acceptance tests agreed before work starts, and every analyst edit is kept and reviewed monthly.

Is this only for earnings-driven hedge funds?

No. The diagnostic starts with your style. A compounder fund gets a thesis record and monitoring; an event fund gets deal-document tools.

Can our analysts maintain it without you?

Training is designed for that. Managed support is optional.

Team

An investor who learned to build, not an engineer who learned finance.

Thesis & Method connects the people who understand the investment question with the engineers who build the system to investigate it.

Founder · Forward-Deployed Investor NL

Founder

Nikola Legetic, CFA

Designed the Forward-Deployed Investor method and the product suite behind it. His role in every engagement is to understand the question beneath the task, make the analytical requirements explicit, and judge the result alongside the analysts who know the companies.

  • 17 years of buy-side equity research; multi-sector analyst at Artisan, GQG Partners, Eagle Asset and RLCM
  • Led the implementation of data analytics, machine learning and AI for Artisan Global Equity
  • Co-founder of NSigma; Head of Product for NSage.ai and NSight Private Credit
  • Columbia Business School, AI for Finance & Business (2026); Claude Certified Associate, Foundations (2026)

LinkedIn profile

NSigma engineering

The depth to build. The discipline to maintain.

An investment workflow is only as dependable as the engineering underneath it. NSigma implements the integrations, calculations, tests and operating controls behind every workflow. Technology choices follow the use case and your environment.

~30

developers, averaging 9 years of specialized experience

PhD

level support in quant research and machine learning

27001

ISO-certified information security management

Vendor-agnostic: built around your needs, not software licenses. Product work includes NSage.ai and NSight Private Credit.

One engagement, clear responsibilities

Who does what.

Forward-Deployed Investor

Defines the question.

Translates research intent into analytical requirements and judges whether the output is useful.

NSigma engineering

Makes it work.

Implements, tests and supports the agreed workflow.

Your firm

Owns the judgment.

Your analysts review the evidence. Your PMs keep every investment decision.

We start up to two new diagnostics a month, so your Forward-Deployed Investor stays on your calendar.

Research standard · Designed to be challenged

An answer is only useful if it survives scrutiny.

Every workflow must make it possible to retrace the evidence, reproduce the calculation, and see where human judgment entered the result.

Source documentEvidence and definitionsCalculation and interpretationHuman reviewAccepted research record
1

Keep the evidence attached.

Outputs lead the reviewer back to the source passage, the reporting period and the definition. A useful summary cannot hide a missing input or a contradictory disclosure.

2

Respect what was knowable.

Historical tests use an explicit information cutoff. A restated filing or revised dataset cannot silently stand in for what the analyst originally had. Where the original vintage is missing, that limitation stays visible.

3

Make calculations reproducible.

Extraction and language tasks can use AI. Arithmetic, reconciliations and model checks run in inspectable code with explicit inputs, units and assumptions. Same input, same answer, every time.

4

Separate fact from judgment.

Confirmed evidence, interpretation and unresolved questions stay distinguishable. A missing figure stays missing until a defensible source establishes it.

5

Preserve the disagreement.

Analyst edits and their reasons are kept and reviewed monthly. If nobody ever disagrees with the machine, nobody is checking it, so an acceptance rate near 100% triggers a spot check.

6

Keep approval with the team.

Drafts and alerts support review. Analysts approve research outputs and model changes. Position sizing, order entry and publishing are never built.

Security and data · For the COO, compliance and the data lead

Your environment. Agreed controls.

Your data rights, research ownership and operating environment are part of the specification. They are set before the build, not after.

Before we start

A mutual NDA is signed before the diagnostic begins. Engagement terms are set out in the proposal.

Deployment

Fund-hosted, managed or hybrid, chosen with your IT to fit your data permissions and existing systems. Access, operating responsibilities and the support route are agreed before release.

Certification

ISO 27001-certified information security management, held by both NSigma operating entities.

Who touches your data

Each engagement has a named team: your Forward-Deployed Investor and the NSigma engineers assigned to it. Only named people get access, inside the environment you choose and under your access controls. Access is logged and removed when the engagement or managed service ends.

AI providers

We work within the AI providers your firm has already approved. For our own build work, we use Claude Code and Codex under business terms that exclude training on your data.

Licensed data

We process only sources you are licensed to use with AI. The diagnostic confirms each one before any build.

Information barrier

Your positions, theses, models and notes are never shared with another client and never used to build or improve NSage, Research OS or any shared component.

Each client works in a separate environment. Reusable components contain no client data.

Personal trading

Anyone on our side with access to your data does not trade individual securities you hold or are researching.

During the engagement and for 30 days after. Diversified funds and ETFs are excluded. Each person attests to this annually.

Ownership

You keep

  • Bespoke deliverables and documentation
  • Client-specific code
  • Your own research and data

Licensed

  • NSage, machine-learning earnings forecasts
  • Research OS, the workflow framework the builds run on
  • Reusable proprietary components

No tool sizes positions, enters orders, publishes, or changes a model without analyst approval.

Your vendor due-diligence questionnaire is welcome. We answer it before the diagnostic starts.

Start with the work

What is your team working around?

A model update that takes too long. A thesis that is hard to monitor. A research question you never get to. Bring one concrete example.

  1. Your investment style, coverage and research process
  2. The recurring work that consumes analyst capacity
  3. The questions you would like to investigate more deeply

You leave with a proposed diagnostic scope, fee and on-site week, or a clear view that it is not the right time.

Book 45 minutes

Or prepare a short introduction and email it to us.

Keep it high-level. No positions or confidential research needed.

Privacy

Privacy notice

How this website and our pre-engagement contact handle personal information. Effective October 25, 2026.

Who we are

Thesis & Method is a research and workflow service provided by NSigma Global LLC, with engineering delivered together with its affiliate NSigma Logic DOO (together, “NSigma”, “we” or “us”). Questions about this notice go to FDI@nsigma.io.

What this notice covers

This notice covers visits to this website, booking a call, and email correspondence before an engagement. Client data processed during an engagement, such as positions, models, notes and licensed data, is governed by the engagement agreement and the mutual NDA, not by this notice. The controls that apply to it are described on the Security page.

What we collect

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Changes

If we change this notice, we will post the new version here with a new effective date.

Terms

Website terms of use

The terms for using this website. Engagements are governed by a separate written agreement. Effective October 25, 2026.

Who operates this site

This website is operated by NSigma Global LLC, a Wyoming limited liability company (“NSigma”, “we” or “us”). Thesis & Method is a service of NSigma. By using the site you agree to these terms.

No investment advice

Thesis & Method provides research process and workflow services only. Nothing on this site is investment, legal, tax or accounting advice, a recommendation to buy, sell or hold any security, or an offer of any product or service. NSigma is not acting as an investment adviser through this site. Company names and tickers appear only to illustrate workflows.

Illustrative content

Figures, charts, grids, research records and case studies on this site use sample or illustrative data unless they are clearly stated to be otherwise. They are not client results, not measurements of any fund and not forecasts or performance claims. Market figures shown as samples may not be current or accurate.

Engagements

Any diagnostic, build, training or managed service is provided only under a separate written agreement and the mutual NDA signed before the work starts. Those documents set the scope, fees, ownership, confidentiality and liability for the engagement and take precedence over anything on this site. Descriptions of scope, timing and pricing here are a summary and may change.

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Changes and contact

We may update these terms by posting a new version here with a new effective date. Questions go to FDI@nsigma.io.

A conversation about your process

Start with 45 minutes on your process.

Bring the workflow your team keeps working around. We begin with the investment question behind it.

Book 45 minutes