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Chapter 01 · The Builder's Ledger

I help B2B SaaS founders build their revenue engine and ship AI features customers pay for.

I'm [PLACEHOLDER: founder name], founder of LogiqCube Consulting. This page is a ledger: what I have built, how I build, and what it costs to start. Proof comes first. You decide what it adds up to.

GTM Engineering AI Engineering

Every engagement starts with a problem. These are the two I hear most.

Chapter 02 · The Problem

Two problems I hear from almost every founder I talk to.

Your pipeline is manual.

  • Leads live in spreadsheets and three different tools.
  • Follow-ups depend on someone remembering to send them.
  • Nobody can say which mailbox or message is working.
How GTM engineering fixes this

Your AI roadmap never shipped.

  • The prototype worked in a demo, not on real customer data.
  • Nobody owns evaluation, cost or failure handling.
  • Your team is busy keeping the core product running.
How AI engineering fixes this

Both are engineering problems. Here is who would be doing the engineering.

Placeholder for a photo of [PLACEHOLDER: founder name]

Chapter 03 · The Builder

I'm an engineer who builds his own SaaS, not an agency.

I am a software engineer with 10+ years in ASP.NET Core, Clean Architecture, SQL Server, Angular and React. I build my own SaaS products, and I use the same engineering on GTM systems and AI features for B2B SaaS founders.

When you hire LogiqCube, you work with me directly. I scope the work, write the code and answer your messages. There is no account manager and no hidden subcontracting.

  1. 01

    Enterprise .NET

    ASP.NET Core and SQL Server systems for business clients

  2. 02

    Architecture

    Clean Architecture, Angular and React front-ends

  3. 03

    Own SaaS

    Built LogiqLead, a multi-tenant outreach product

  4. 04

    AI products

    Built LogiqAIAssist; building LogiqReview AI

  5. 05

    LogiqCube

    The same engineering, for B2B SaaS founders

Ten years of shipping taught me one rule: decide what to build before building it.

Chapter 04 · The Method

AIDD: four steps from idea to a system you own.

Audit, Isolate, Design, Deliver. Every engagement follows the same order, so you always know what comes next and what you get at each step.

Read the full method

  1. Step 1: Audit

    I map what you have: tools, data, codebase and the one number you want to move.

    Output → Audit report with findings and risks

  2. Step 2: Isolate

    We pick the single workflow or feature with the fastest payback and agree what "done" means.

    Output → Scoped brief with acceptance criteria

  3. Step 3: Design

    I write the architecture, data contracts and test set before code, so cost and risk are visible early.

    Output → Architecture document and test set

  4. Step 4: Deliver

    I build in your repository with weekly Loom updates, a demo at each milestone and full handover docs.

    Output → Working system, runbook and handover

A method only matters if it ships. Here is what it shipped.

Chapter 05 · The Proof

Three products I built myself, with an honest status on each.

These are my own products, not client work. Each case study shows the problem, the architecture and the decisions, so you can judge the engineering directly.

  • Placeholder for the LogiqLead prospecting module screenshot
    GTM EngineeringStatus:Demo

    LogiqLead

    A B2B lead generation and outreach SaaS with prospecting, sequences, reply detection and a mailbox deliverability audit.

    Answers: Your pipeline is manual.

    Read the case study for LogiqLead[PLACEHOLDER: demo URL]
See all work

Each of those builds maps to a service you can hire.

Chapter 06 · The Services

Two services, each made of modules you can start with one at a time.

GTM Engineering

I build the systems behind your pipeline, from lead data to outbound automation, CRM workflows and GTM analytics, so outbound runs without a founder doing it by hand.

  • Lead data pipelines

    Pull, clean, de-duplicate and enrich lead data into one source of truth your team trusts.

  • Outbound automation

    Sequences, follow-ups and reply handling that run on schedule and stop when a prospect answers.

  • CRM and workflow integration

    Connect your CRM, inbox and internal tools so status, notes and next steps sync without copy-paste.

  • AI-personalized outreach

    First lines and messages drafted from real prospect data, with a human approval step.

  • GTM analytics

    Reply rates, mailbox health and pipeline movement in one view you can act on.

Explore GTM Engineering

AI Engineering

I add production AI features to your existing SaaS, from RAG over your customers' documents to AI workflow automation, with evaluation and guardrails built in from day one.

  • RAG systems

    Question answering over your customers' documents, with tenant isolation and cited sources.

  • AI features in existing apps

    AI features built into your current codebase and data model, not a separate demo app.

  • AI workflow automation

    Model-driven steps inside existing workflows, such as triage, extraction, drafting and review.

  • LLM integration architecture

    A provider-agnostic model layer, prompt management, cost controls and structured outputs.

  • Evaluation and guardrails

    Test sets, quality checks and fallbacks so you know when an AI feature is good enough to ship.

Explore AI Engineering

You do not have to commit to a full build to find out if this works.

Chapter 07 · Entry Offers

Start small, paid and fixed-scope.

Both entry offers have a fixed price and a clear output, so you can judge my work before committing to a larger build.

  • GTM Engineering · 1 week

    GTM Audit

    A fixed-scope review of your lead data, outbound tools and deliverability.

    Starting from[PLACEHOLDER: USD price]

    • Map of your current GTM tools and data flow
    • Mailbox and domain deliverability check
    • Prioritized build plan with effort estimates
    • Walkthrough call and written report
    Start with the GTM Audit

  • AI Engineering · 2 weeks

    AI Feature Sprint

    One AI feature, built against your real data and measured on a test set.

    Starting from[PLACEHOLDER: USD price]

    • Feature scoping and success criteria
    • Working version in your repository
    • Evaluation against an agreed test set
    • Production plan with cost estimate
    Start with the AI Feature Sprint
See all offers and pricing

A small first project reduces one risk. These terms reduce the rest.

Chapter 08 · The Terms

Terms that keep the risk on my side.

  • Full IP transfer

    You own the code, docs and designs as each milestone is paid.

  • NDA before discovery

    I sign your NDA, or send mine, before you share anything sensitive.

  • Milestone billing

    You pay per agreed milestone, not a large upfront retainer.

  • USD / EUR invoicing

    Invoices in USD or EUR, paid via Wise, Stripe or Payoneer.

  • US / EU timezone overlap

    Daily overlap hours with US and EU teams for live calls.

  • Registered partnership firm (India)

    Contracts and invoices come from LogiqCube Consulting.

Here is how a first project becomes a working relationship, if you want one.

Chapter 09 · How We Engage

Discovery, then Build, then Retainer.

Each step is a separate decision. You never commit to a large project before you have seen how I work.

How we work together

  1. Discovery

    A paid, fixed-scope entry offer: the GTM Audit or the AI Feature Sprint. You get a plan or a working feature, and no obligation to continue.

  2. Build

    A scoped build with milestones. Code goes into your repository from day one, and you pay per milestone delivered.

  3. Retainer

    Optional. A monthly retainer for improvements and maintenance once the system is live.

I am early in taking on clients, and I would rather say so plainly.

Chapter 10 · Founding Clients

That is the whole ledger. One decision is left.

Chapter 11 · The Ask

Pick the problem you want solved first.

Products built and shown
3
Method steps, in order
4
Paid entry offers
2
Code ownership
Yours
Lock-in
None
Status of each build
LogiqLead Status:Demo
LogiqAIAssist Status:MVP
LogiqReview AI Status:In Build

If: Your pipeline is manual.

Build your revenue engine

Start with a one-week GTM Audit of your lead data, outbound tools and deliverability.

Book a GTM Audit About GTM Engineering

If: Your AI roadmap never shipped.

Ship your AI feature

Start with a two-week AI Feature Sprint: one feature, built and measured on your data.

Book an AI Feature Sprint About AI Engineering
GTM AuditAI Feature Sprint