Integration-first AI systems

SMBs waste £50K+ on AI projects that go nowhere.

We design, build, and ship AI systems in 4–6 weeks – plugged straight into the tools and workflows you already trust.

No lab experiments. No platform migrations. Just systems that genuinely slot into your workflow and make day-to-day work lighter.

Live in 4–6 weeks Tool-agnostic · GCP, Sheets, CRMs, custom stacks

What you actually get with Nativly

  • A working AI system in 4–6 weeks.
  • Built directly into your existing tools and workflows.
  • Clarity on what will actually work inside your systems — not more noise.
Led by Razvan Burlacu & YJ Chen – 20+ years integrating new technology into existing systems — combining commercial strategy with hands-on delivery.

Why most AI projects never make it to production

You’re not the only one. Most SMBs burn budget on proofs-of-concept that never turn into working systems.

💸

Enterprise consultancies, enterprise pricing

Big firms want £200K+ and 6 months before you see anything real. By the time a slide deck lands, your team has moved on.

🧩

No clear “first move”

You know AI matters, but it’s not obvious where to start, how to scope it, or how to tell if it’s actually working.

🧰

Tools that sit outside the work

Standalone dashboards and ChatGPT links don’t change behaviour. If it doesn’t live in your CRM, Sheets, or ops tools, it won’t be used.

🌀

Too much noise and not enough actual expertise

The AI space is crowded with “consultants” who can talk about AI but can’t ship systems. Most advice sounds promising but doesn’t survive contact with your actual tools, data, or constraints. You need clarity, not more noise.

Who we’re a good fit for

We work with teams who don’t have time for experiments – they need working systems plugged into real revenue and operations.

Owner & leadership
Founders & MDs who want AI that pays for itself

You’re responsible for the P&L and don’t care about hype. You need practical systems that lighten the load, reduce friction, and actually get used.

Typical outcome: first system live in 4–6 weeks
Ops & data
Operations leads and overstretched CTOs

Your backlog is full and the team is small. You need a partner who can design, build, and own a scoped AI system end-to-end.

We plug into your stack, not replace it
Investors & portfolio
Investment firms with complex portfolios

You need live visibility across brands, markets, and operators – without forcing everyone onto one tool. We build data and AI layers around what’s already there.

From spreadsheets to live portfolio intelligence

How we work together

Three stages. Clear scope. Concrete deliverables. You always know what’s coming next.

1

Map

We map your workflows, tools, and constraints, then identify 2–3 opportunities that will genuinely work inside your real systems — not just sound interesting. You leave with a complete system map showing the architecture, integrations, data flows, and a clear build roadmap you can use with us or your own team.

1 week · remote sessions
2

Pilot

Using the system map from Step 1, we design and ship the first working version of your system — either alongside your team or independently. This is a scoped, integration-ready build based directly on the architecture we defined together.

4–6 weeks · fixed scope
3

Scale

Once the mapped system is live, we focus on ongoing wins inside your existing infrastructure — strengthening what’s working, rolling out to more teams, and adding new use-cases that naturally fit your stack.

Ongoing · only if it earns its keep

Systems we’ve actually shipped

Real client systems, NDA-safe. Short on the surface, technical when expanded.

Case study · Market expansion

UK Market GTM Readiness AI

A highly personalised AI agent supporting experienced professionals doing GTM work between Asia and the UK — turning multi-day research into same-day decisions.

LLM Agent Document Ingestion Tool Calling UK GTM Intelligence

What changed

  • Replaces 20–30 hours of repetitive GTM desk research per brand.
  • Converts 3–6 days of manual analysis into a repeatable, same-day workflow.
  • Surfaces UK positioning gaps early, reducing wasted localisation and launch effort.
Expand details
  • Designed as a client-specific, trained AI agent, not a generic assistant.
  • Ingests decks, websites, competitor material, and supporting documents as part of a structured pipeline.
  • Encodes domain-specific GTM heuristics (UK market context, category norms, positioning signals).
  • Produces consistent, decision-ready outputs aligned to how the professional already works.
  • Operates as a drop-in replacement for repetitive analyst desk research, without workflow disruption.
Case study · Professional services

Trust & Footprint Intelligence System

An orchestrated system of AI agents that compresses consultant screening from 30–60 minutes to under 5, while preserving senior judgment.

RAG Multi-Agent Orchestration Structured Scoring Slack Alerts

What changed

  • First-pass screening drops from 30–60 min to <5 min per consultant.
  • Senior reviewers focus only on flagged outliers, not routine triage.
  • Consistent scoring and thresholds across large consultant cohorts.
Expand details
  • Built as a multi-agent system, not a single monolithic model.
  • Agents operate over a RAG-based consultant knowledge base (CVs, portfolios, websites, references, supporting docs).
  • Outputs structured profiles and scoring artefacts into internal databases in Markdown.
  • Explicit separation between automated signal extraction and human judgment.
  • Slack-based escalation for anomalies and edge cases.
  • Designed to scale throughput while increasing consistency and auditability.
Case study · Investment firm

Cross-Vertical GCP Data Platform

Modernised a decade-old server-based estate into a unified data foundation — enabling real-time visibility and positioning the organisation for autonomous decision support.

BigQuery dbt Metabase Legacy Migration

What changed

  • Eliminated 1–2 days of recurring manual consolidation per reporting cycle.
  • Unified fragmented systems into a single source of truth.
  • Reduced reporting latency and reconciliation risk across verticals.
Expand details

Foundation layer for the decision system below. In this specific case, maximising impact required modernising infrastructure first.

  • Migrated legacy 2012-era self-hosted VMs and databases into GCP (BigQuery).
  • Introduced a dbt transformation layer to standardise metrics across retail, education, and other business units.
  • Implemented a clean separation between raw, transformed, and analytics-ready data.
  • Added Metabase for rapid executive-level visualisation and portfolio visibility.
  • Designed explicitly to support downstream AI and decision systems — not just reporting.
  • This infrastructure work was a prerequisite to unlocking real autonomy in the organisation.
Case study · Investment firm

Decision & Query Layer (NL → SQL → NL)

The culmination of the system rebuild — role-based decision support and natural-language querying that removes dependency on analysts.

NL→SQL BigQuery Chat Role-Based Access Decision Support UI

What changed

  • Eliminated the ad-hoc SQL request bottleneck.
  • Returned analyst time to core work instead of constant one-offs.
  • Enabled faster, more confident decisions at every level.
Expand details

Built on top of the data foundation above. After mapping the organisation’s systems and modernising the platform, we expanded into a role-specific decision layer.

  • Role-specific frontend views for executives, managers, and operators.
  • Authentication and access control aligned with IAM and GDPR requirements.
  • Natural-language → SQL → natural-language chatbot directly integrated with BigQuery.
  • Covers edge questions not anticipated in dashboards or KPIs.
  • Frontend sits both upstream (capturing inputs) and downstream (distributing insight).
  • Represents the full journey: system mapping → infrastructure modernisation → autonomous decision capability.

Who you’ll work with

Senior hands-on operators, not a rotating cast of juniors.

YJ

Commercial & strategy

YJ Chen

Commercial & Strategy Lead

15+ years in retail, immersive technologies, and multi-market expansion. Adept at mapping workflows to identify AI systems that deliver immediate operational value. Advisor to UK, Vietnam, and Taiwan enterprises. MBA from London Business School. MIT AI Executive Programme. Master of Industrial & Operations Engineering from University of Michigan Ann Arbor.

RB

Systems & delivery

Razvan Burlacu

Technical Lead

10+ years in immersive technologies, AI SaaS, and workflow systems engineering. Specializes in system architecture and rapid delivery of production-ready AI tools, integrating them into existing environments. Advisor to startups and SMEs with a proven track record of successfully delivering integration-first systems spanning CRMs, ERPs, Sheets, and custom stacks. Known for practical solutions deployed in weeks.

Common questions

A quick sense-check before you book a call.

What happens on the first call?
We’ll map 2–3 concrete opportunities where AI can help, based on your current tools and workflows. No homework required – you bring context, we’ll bring structure.


What if we’re not “ready” for AI?
Most teams feel that way. As long as you have some digital workflows (Sheets, CRMs, ERPs, dashboards), there’s usually a sensible starting point.

A scoped AI system, wired into your tools, plus a short enablement session for your team. You keep the system and documentation, regardless of whether we continue together.
No. We’re stack-agnostic. Recent work includes GCP (BigQuery, dbt, Metabase), custom web apps, as well as stacks built around Google Sheets, Notion, and CRMs. The right stack is the one your team will actually use.
We work within your existing security model: VPNs, SSO, service accounts and principle of least privilege. We agree access levels up-front and can work with your IT or security partner where needed.
We sometimes run strategy and architecture engagements, but our bias is towards systems that ship. If you’re not ready for a build, we can design a roadmap that your internal or existing partners can execute.

Ready to explore a real use-case?

In 30 minutes we’ll shortlist 2–3 sensible AI opportunities for your business – and tell you honestly if now isn’t the right time.

Book the 30-minute AI opportunity call →