Your AI team.Not another agency.

We build the AI most likely to move your revenue, inside the operation you run or the product you sell, without you hiring an ML team.

Book a 30-minute callSee our work

Two beliefs.

Every company is becoming an AI company.

Almost none of them are ready.

We close that gap. First system live in 90 days, measured against a number that matters — not a deliverable — before you spend months figuring out where to start.

We do two things.

Two ways we work. The build inside each one is different for every team.

How it works.

Most teams start with the tool. We start at the result you are paid for, find the one bottleneck in the way, and build the smallest live system that moves it.

  1. Set the North Star

    We start with the number that makes the work worth doing: revenue recovered, qualified pipeline opened, conversion lifted, margin protected, or a paid AI capability your customers will use.

  2. Name the constraint

    Then we find the one thing blocking it: slow follow-up, scattered data, manual handoffs, approval rules, or a product team that cannot staff the AI build this quarter.

  3. Work backwards to today

    That gap becomes the implementation plan: what goes live first, what data it needs, who approves it, what can fail, and which metric tells us it is working.

  4. Build the first system

    We build inside the stack you already run or the product your customers already use, so the AI lives where the money or product decision happens.

  5. Compound the gains

    Once live, the system learns from approvals, objections, edge cases, failures, and model changes. Every week in production it does more of the job.

Case studies.

Real systems from both sides of the lab: operations that needed AI in the work, and product teams that needed AI capability without building an ML team first.

Company Brain

Scattered institutional knowledge became a single source of truth the whole company can query.

Inbound Triage

Reply times that ran in hours now run in minutes, with every message staged for human approval.

Content Production System

One brief in, finished output out, brand control intact.

Lead Intelligence

Every prospect enriched and a drafted message staged before a rep opens the account.

Custom LLM Build

A domain model trained from scratch on a 143-million-token corpus, not a wrapper over a frontier API.

Proptech Platform

1.4 million transactions turned into a defensible valuation engine.

Email Marketing SaaS

Brand-safe AI composing inside the product, with a vision gate before anything reaches a customer.

On-Device Vision

Studio-grade capture consistency from a phone, no cloud pipeline, no new hire.

Model Infrastructure

Model strategy became a setting to tune, not a release to fear.

Bring the thing that's stuck. Leave knowing what we'd build first.

In thirty minutes we'll work out what to build, what number it has to move, and whether it's worth paying us to do it. If it isn't, I'll tell you.

Book a 30-minute callEmail the founder
Imraan Habib, founder of twohundred.ai

Imraan, Founder

I spent ten years running operations. I know what it costs to lose a booking because the inbox sat unread for six hours. I built twohundred to be what I'd have wanted back then, from someone who's been on your side of the table. I take the calls myself.

twohundred.aiBook a call
twohundred.ai — AI implementation and integration for business

Your AI team.Not another agency.

We build the AI most likely to move your revenue, inside the operation you run or the product you sell, without you hiring an ML team.

See our work

Two beliefs.

Every company is becoming an AI company.

Almost none of them are ready.

We close that gap. First system live in 90 days, measured against a number that matters — not a deliverable — before you spend months figuring out where to start.

We do two things.

Two ways we work. The build inside each one is different for every team.

01Operational

AI inside the operation you already run.

We build the AI layer around the systems that already make you money: lead handling, inboxes, approvals, reporting, and team handoffs. Not a demo. A workflow your team can actually run every day.

CRM, email, calls, docs, dashboards, approvals.

01 Operational. AI inside the operation you already run. Leads reach the right person before they go cold. The inbox answers itself and waits for your nod. Quiet accounts get worked again, and the answer your team needs is one question away. Not the AI toggle your software already sells you: built for the edge cases, the messy data, and the approval rules only your business has.

02The Lab

Your full-time AI team, building alongside your own engineers.

We work inside your codebase, data model, and runtime to build the AI features your product team cannot staff this quarter. Evaluated, monitored, and migrated as models change. Not a vendor bolting an API on from the outside.

Embedded. Production-grade. Confidential by default.

02The Lab

Your full-time AI team, building alongside your own engineers.

We join your product team as the AI implementation bench: features, retrieval, agents, evals, monitoring, and model migrations inside your existing codebase. Your roadmap moves without hiring a new ML squad first.

Embedded. Production-grade. Confidential by default.

Most teams start with the tool. We start at the result you are paid for, find the one bottleneck in the way, and build the smallest live system that moves it.

Start at the 12-month North Star

  1. Set the North Star

    We start with the number that makes the work worth doing: revenue recovered, qualified pipeline opened, conversion lifted, margin protected, or a paid AI capability your customers will use.

  2. Name the constraint

    Then we find the one thing blocking it: slow follow-up, scattered data, manual handoffs, approval rules, or a product team that cannot staff the AI build this quarter.

  3. Work backwards to today

    That gap becomes the implementation plan: what goes live first, what data it needs, who approves it, what can fail, and which metric tells us it is working.

  4. Build the first system

    We build inside the stack you already run or the product your customers already use, so the AI lives where the money or product decision happens.

Compound the gains

Once live, the system learns from approvals, objections, edge cases, failures, and model changes. We tighten the prompts, evals, routing, and business rules so every week in production it does more of the job and less of it lands on your team.

Real systems from both sides of the lab: operations that needed AI in the work, and product teams that needed AI capability without building an ML team first.

0 daysto first live system
0confidential builds
0M+training corpus tokens
Cross-tool company memory and retrieval layer
Company Brain

Turning scattered institutional knowledge into a single source of truth: private retrieval across every call, ticket, document, and CRM record, answering in plain language with the exact citation behind each line. The context that once lived in people's heads became a system the whole company can query.

Inbound triage automation inside the inbox
Inbound Triage

Reply times that ran in hours now run in minutes, and inquiries stopped slipping through: full-volume inbox triage reads, qualifies, and drafts a response to every message the moment it lands, then stages it for human approval inside the mailbox the team already works in. Faster replies, no missed inquiries, more of them converting to booked revenue. No new tool to adopt.

Content production system with human approval gates
Content Production System

Creative work that was manual and hard to scale became a controlled production system: scene planning, image generation, animation, assembly, and publishing, each step routed to the right model and every output held for human approval. One brief in, finished output out, brand control intact.

Account research and outbound intelligence
Lead Intelligence

The qualification and personalisation a team did by hand now happens before a rep opens the account: every prospect enriched, the signals that predict real intent extracted, and a drafted outbound message staged in a single approval queue.

Custom language model trained from scratch
Custom LLM Build

A domain-specific language model trained from scratch on a 143-million-token corpus, not a wrapper over a frontier API. Constrained decoding validates every figure against real data before it is returned, deployed in production as the conversational layer the product is built around.

Proptech valuation model ensemble
Proptech Platform

The intelligence layer a data-rich platform had no ML team to build: a four-model ensemble over 1.4 million transactions and 146 features that returns a defensible valuation, with an agreement scorer that flags when the models diverge. A proprietary data moat turned into a working pricing engine.

AI email generation inside a SaaS product
Email Marketing SaaS

Brand-safe generative AI built natively into the product, composing copy section by section from a learned brand-voice profile. An OCR and vision evaluation gate inspects every rendered email and blocks a brand or layout break before a single customer can see it, making generation at scale safe to trust.

On-device computer vision capability
On-Device Vision

A computer-vision capability the product team had no engineers to build, delivered entirely on-device: pose and lighting detected in real time and every capture auto-aligned to the last, with no cloud pipeline and no new hire. Studio-grade consistency from a phone in the user's hand.

Model routing and evaluation infrastructure
Model Infrastructure

The layer that lets a product change models without the product feeling it: routing behind a stable user path, regression and score-threshold gates that catch silent quality drift, and budget ceilings with a deterministic fallback. Model strategy became a setting to tune, not a release to fear.

In thirty minutes we'll work out what to build, what number it has to move, and whether it's worth paying us to do it. If it isn't, I'll tell you.
Email the founder
Imraan Habib, founder of twohundred.ai

Imraan, Founder

I spent ten years running operations. I know what it costs to lose a booking because the inbox sat unread for six hours, or to pay an agency a retainer for reports nobody opens. I built twohundred to be what I'd have wanted back then, from someone who's been on your side of the table. I take the calls myself.

We scope pricing to the specific build on a 30-minute call, once we both know what we are solving. Monthly rolling, no annual contract, no setup fee, no minimum term. If the work hasn't moved a metric that matters to you by month three, you walk and we wish you well.

Most AI agencies sell chatbots, Zapier workflows, or dashboards. We don't. We build AI that runs inside the tools your team already uses, whether that is your CRM and inbox or your codebase and product surfaces. No new platform, no training day, nothing for your team to adopt. And the people building it have actually run businesses, not just consulted on them.

Two kinds of work. For operators running a business: lead routing, reactivation, answer-engine positioning, and content that compounds. For product teams building one: AI capabilities inside your stack, built and run as if we were your ML team. Both run on the same 90-day commitment.

Yes. This is what the AI Integration Lab does. We operate as your outsourced AI integration function, wiring foundation models into your product as live modules. New capability every eight to twelve weeks, frontier migrations handled on retainer, most engagements confidential. You keep building the product, we build the AI inside it.

Generative Engine Optimization is the practice of making your business clear enough for AI answer engines like ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews to understand, trust, and cite. SEO still matters, but GEO adds answer-first pages, structured data, consistent definitions, and credible external signals that help AI systems know when your company belongs in the answer.

No. That's the whole point. Every build is designed to reduce work, not add it. No new logins, no new dashboard to check, no retraining. If anything, the person who usually holds everything together gets a few hours a week back.

Most clients see a working outcome within 90 days. For operators that is usually revenue moving. For product teams it is a capability live in production. Either way we plug into what you already have, no lengthy implementation, no change management.

Two kinds of team. Operators running businesses that have outgrown spreadsheets but aren't ready to hire an in-house tech team. Hospitality, fitness, agencies, professional services, real estate, e-commerce. And product teams who need AI capabilities in their stack without the six-month hire-and-ramp of building an ML team. The common thread isn't industry, it is people who want reliable execution, not reports.

We do. Every system we build has a human review step at the points where getting it wrong would cost you money. A BDM approving a draft reply. An operator confirming a campaign before it sends. A review response queued before posting. The AI does the work. Your team decides what goes out. And we're on the hook every month to keep it sharp.