Straight answers to the questions that matter.

A production-ready AI app built by a specialist agency typically costs $25,000-$60,000 (£20,000-£48,000) over 6-10 weeks. Simple AI MVPs run $8,000-$20,000, while multi-agent or enterprise systems start at $60,000 and can exceed $500,000. Budget 15-25% of build cost annually for inference and maintenance.

The four biggest drivers are AI complexity (a single API call vs a multi-step agent), data and RAG architecture, integrations with your existing systems, and who builds it. Hiring in-house can cost 3-5x more than a specialist agency once salaries, benefits, and the 3-6 month ramp-up are included.

Plan for $500-$3,000 per month on a typical production app — model API calls ($50-$10,000 by volume), cloud hosting ($20-$200), a vector database, and observability. Enterprise systems with high inference volume run $3,800-$30,000+ monthly. Budget these before launch, not after the first cloud bill.

A senior AI engineer costs $150,000-$220,000 per year in the USA or UK before hiring costs and the 3-6 months it takes to ramp up. A specialist agency ships a production AI app in weeks for $15,000-$60,000 with full code ownership. In-house only becomes economical once you have sustained volume.

An AI automation MVP runs $20,000-$40,000 (£16,000-£32,000). Mid-level platforms cost $40,000-$80,000 and enterprise automation platforms $80,000-$150,000+. Add LLM API usage of $500-$15,000 per month and infrastructure. Building beats buying when your workflows are custom or your data is sensitive.

AI automation uses machine learning, language models, and AI agents to run business workflows without a human at every step. Unlike scripted automation, it reads documents, makes decisions, and adapts when inputs change. Most teams start with one high-value workflow — invoices, support tickets, or data entry — before scaling to multi-agent systems.

A chatbot answers questions. An AI agent takes action — it plans, calls your APIs, updates records, and completes multi-step tasks. Three things make an agent an agent: it uses tools, holds memory across steps, and decides its next action from results. Chatbots are cheaper for simple Q&A; agents are for workflows that end in a real-world action.

RAG (retrieval-augmented generation) searches your documents at query time and feeds results to the model, so answers stay grounded in your data and current. Fine-tuning changes the model's weights to match your tone and format. Start with RAG — it is faster to build and cheaper to run. Fine-tune when you need consistent behaviour or sub-200ms latency. The best systems use both.

LangChain is a framework for orchestrating LLM workflows — chaining prompts, tools, and retrievers into pipelines. It is ideal for prototyping RAG systems and agentic workflows quickly. LangGraph, its successor, adds stateful graphs for production multi-agent systems. We use it where it fits; for simple integrations a direct API call is often cleaner and cheaper.

Enterprise AI is built in layers: data access and pipelines, approved foundation models, security and governance (RBAC, audit logs, guardrails), and application patterns like RAG and agents. Most of the effort goes into data quality, integration with systems of record, and compliance — not the model. We map to NIST AI RMF and ISO/IEC 42001 where required.

We take an idea to a production AI SaaS in four phases: discovery and scoping, a working prototype in about three weeks, a production build with multi-tenancy, auth, billing, and observability, then launch with monitoring. Typical build time is 3-6 months depending on scope. You own the code and IP throughout.

A custom AI system starts with workflow mapping and a data audit, then a thin-slice prototype on your data in 2-3 weeks. The production build covers model selection, RAG or agents where needed, integrations, evals, guardrails, and monitoring. We ship with fallbacks and graceful degradation so the system keeps working when a vendor or model fails.

A prototype lands in about three weeks. A production AI MVP typically takes 6-10 weeks; multi-agent or enterprise systems run 3-5 months or more. You see a live demo every Friday from the first week, so you are never months into a project without seeing real software.

Five steps: discovery and workflow audit, a fixed-scope prototype on your data, a production build with evals and guardrails, deployment on your infrastructure, and ongoing monitoring and support. Weekly demos and a shared progress dashboard keep scope, spend, and progress transparent at every stage.

We work asynchronously with a shared channel, daily updates, and a live progress dashboard, plus a weekly demo you can join from any time zone. A senior engineer replies within 24 hours, so USA and UK teams never wait for a hand-off between continents.

Yes — always. Full IP transfer, clean documentation, and no lock-in. Your code, your data, and your models are yours from day one. We help you run it or hand it to your team. Your data is never used to train foundation models.

Yes. We document everything and structure the codebase so your team can take over cleanly. Many clients start with us and gradually build an internal team. We support the transition — onboarding docs, architecture handover, and as much or as little post-handoff support as you want.

Yes. Every build ships with SOC 2 Type II patterns, HIPAA-ready PHI/PII redaction with AES-256-GCM encryption, and CCPA/CPRA-ready data handling — RBAC, audit logs, and deletion flows enforced in code. Data processing agreements are signed before work starts, and we deploy on your VPC or data center when required.

Your data is never used to train foundation models. Production systems self-host inference where it matters, so your documents stay inside your boundary. Encryption is AES-256-GCM at rest and TLS in transit, with keys managed through a proper KMS. You can deploy privately, on-prem, or even air-gapped.

Prototype first, on your data, for a fixed small scope. You see working software in about three weeks, and independent penetration tests and source reviews are welcome at any stage. Only then do you commit further — you decide, not us.

We follow UK GDPR and the ICO's AI guidance on every build: a documented lawful basis for data processing, a DPIA for high-risk AI, data minimisation and pseudonymisation before data reaches any model, and automated-decision logging where required. UK clients keep EU-region data residency by default.

The EU AI Act reaches UK companies when AI is placed on the EU market, its output is used in the EU, or it affects EU residents. We classify each system against the Act's risk tiers, apply Article 50 transparency for AI-generated content, and engineer human oversight for high-risk use cases.

Section 80 of the DUAA, in force since 5 February 2026, replaced UK GDPR Article 22 on automated decision-making. Significant automated decisions now need transparency, human oversight, and a right to contest the outcome. The ICO's statutory code of practice took effect in May 2026 — we build these controls in as standard.

Yes. The AI++ is registered in England and Wales (THEAIPLUSPLUS LTD, company no. 17397374) and serves clients across the USA, UK, and worldwide. A senior engineer replies within 24 hours across time zones, and collaboration runs on a shared channel, weekly demos, and a live progress dashboard.

A senior pod is 2-4 senior engineers who own your project end-to-end — the same people who demo to you write the code. No account-manager theater, no hand-offs to a junior bench, and no black boxes. You talk directly with the engineers building your product.

We are AI-native. We build and run our own production AI products — including a world-record 193ms AI calling agent — and measure them against hard latency and cost targets. That discipline applies to every client build: evals, guardrails, observability, and security by default.