The timeline for AI implementation depends on the complexity of the project and how it integrates with your existing systems. Simple AI tools, like chatbots or workflow automation, can be deployed in a few weeks, while more advanced solutions – such as predictive analytics, custom machine learning models, or AI-driven decision systems – typically take 3 to 6 months.

We break the process into clear phases:

1. Discovery & Planning (2–4 weeks) – Understanding business needs, assessing data readiness, and defining success metrics.
2. Development & Integration (4–16 weeks) – Building AI models, integrating with existing systems, and ensuring security and compliance.
3. Testing & Deployment (4–8 weeks) – Validating AI performance, refining workflows, and training your team for successful adoption.
4. Optimisation & Support (Ongoing) – Monitoring AI models, retraining as needed, and ensuring long-term efficiency.

During the consultation phase, we’ll outline a customised timeline based on your specific goals and infrastructure to ensure smooth implementation.

In most cases, you don’t need to replace your existing systems. Our AI solutions are designed to integrate with your existing infrastructure, running on lightweight models and pipelines that have minimal impact on performance. We may recommend minor upgrades, such as additional compute power or cloud resources, to support AI workloads (like a slight server increase, micro service provision or database increase), but these are typically small adjustments rather than major system overhauls. During the planning phase, we’ll assess your setup and ensure the integration is as seamless and efficient as possible.

Ensuring data security and privacy is paramount in our AI integration processes. Beyond adhering to industry-leading standards, we implement comprehensive measures to protect your data throughout its lifecycle:

1. Secure Data Pipelines: We establish robust safeguards at the initial data ingestion point and maintain security throughout the data flow. This includes implementing encryption protocols and access controls to prevent unauthorised data access or tampering.
2. Private AI Models: Our AI models are developed and deployed within isolated environments to ensure that sensitive data remains confidential.
3. Authenticated Model API Calls: We enforce strict authentication mechanisms for all API interactions with our AI models.
4. Data Encryption: All data, whether at rest or in transit, is encrypted using advanced encryption standards. This practice ensures that even if data is intercepted, it remains unreadable and secure.
5. Continuous Monitoring and Auditing: Our systems are equipped with real-time monitoring tools to detect and respond to any unauthorised access attempts or anomalies.

By integrating these practices into our AI solutions, we ensure that your data remains secure and private, aligning with the highest industry standards.

You do. Everything we build, including the code, workflows, agents, and integrations, is yours outright. It is an asset you own, not a licence agreement.

The only exception is the underlying foundation model (such as OpenAI or Anthropic), which remains owned and hosted by its provider, much like running software on cloud infrastructure you do not own.

Your data also stays strictly yours. It runs through the system solely to execute tasks, is never used to train third-party models, and remains entirely under your control.

Support continues after launch. Every build includes a defined support period covering fixes and queries, and most clients transition to an ongoing support plan afterwards for maintenance, updates and change requests. The same in-house team that delivered the build also provides this support, ensuring continuity rather than requiring a new provider to be briefed on an unfamiliar system.

We treat platform and dashboard security as an ongoing practice, not a one-off step completed at launch. That covers:

• Patching and updates: keeping the frameworks, libraries and server images a dashboard runs on current, so known vulnerabilities are patched before they can be exploited, not after.
• Access control: permissioning by role, so each user and each connected system only has access to what it needs, with access reviewed periodically rather than forgotten about.
• Credential and secrets management: API keys and service account credentials rotated on a schedule, not left static indefinitely.
• Monitoring and logging: an audit trail of who accessed what and when, with alerts on any activity that looks unusual. 
• Encryption: data is encrypted both in transit and at rest, keeping it secure throughout the entire system, not just at entry.
• Scheduled reviews: a periodic vulnerability scan or manual review, depending on the risk profile of the system, rather than security being something only addressed at launch.

Security comes built into our ongoing support. The same team that engineered your system manages it, so keeping it secure isn’t an afterthought or an extra contract.