AI Agents & Automation

Traditional automation (RPA, workflow tools) executes rigid, pre-defined steps. If something is unexpected, it fails. AI agents are fundamentally different - they observe their environment, understand context, make decisions, and adapt their approach in real-time.

Traditional Automation: If customer email arrives, extract order number, look up in system, send response. If the format is slightly different, the system breaks.

AI Agent: Reads the email, understands the intent (even if phrased differently), considers the customer's history, checks inventory, pricing, and policies, then drafts an intelligent response that feels personalised and helpful.

AI agents can handle ambiguity, learn from outcomes, and improve over time. They work in messy real-world scenarios where rule-based automation fails. This is why agentic AI is fundamentally more powerful - and why it requires careful design for safety and reliability at scale.

AI agent development pricing depends on complexity, number of capabilities, and integration requirements:

AI Agent Discovery & Strategy: $15K-$25K - Strategic assessment of where autonomous agents add value to your business, technical architecture options, integration requirements, and realistic investment estimates.

Single AI Agent (Focused Scope): $80K-$150K - One autonomous agent handling a specific process (e.g., customer service, lead qualification, invoice processing). Timeline: 12-16 weeks.

Multi-Agent System: $200K-$400K - Multiple agents that orchestrate together, hand off tasks, and collaborate to solve complex workflows. Timeline: 16-24 weeks.

Enterprise Agent Platform: $400K-$700K+ - Comprehensive agent infrastructure for your organisation, integration with multiple systems, advanced monitoring and governance. Timeline: 6-12 months+.

We structure pricing around clear deliverables and milestones, so you know exactly what you are investing and when you can realise value.

Single AI Agent (Proof of Concept): 8-12 weeks. Focused scope, existing frameworks, minimal customisation. Good for validating the concept and business case.

Production AI Agent: 12-16 weeks. Comprehensive testing, safety guardrails, monitoring infrastructure, integration with your systems, production deployment.

Multi-Agent System: 16-24 weeks. Multiple agents that coordinate, orchestration logic, complex integrations, sophisticated monitoring.

Enterprise Platform: 6-12 months+. Multiple agents, extensive system integration, custom governance frameworks, team training and support.

Timeline variability is driven by integration complexity and existing infrastructure. Simple systems that integrate with well-documented APIs move faster. Legacy system integration or custom data pipelines extend timelines substantially. We assess integration points during discovery and provide realistic timelines upfront.

Any business process that involves decision-making, context understanding, and multi-step workflows can benefit from AI agents:

Customer Service & Support: Autonomous response to customer inquiries, intelligent routing to human agents, complaint resolution, refund decisions. Reduces support costs by 40-60%, improves response times dramatically.

Sales & Lead Management: Autonomous lead qualification, customer engagement (follow-ups, personalised outreach), proposal generation. Accelerates sales cycles and improves conversion rates.

Operations & Finance: Invoice processing and categorisation, expense claims, purchase order generation, compliance checking. Reduces manual data entry by 80%+.

HR & Recruitment: Resume screening and candidate ranking, offer generation, onboarding automation, leave request processing.

Content & Knowledge Management: Autonomous documentation, knowledge base creation, content moderation, policy summarisation.

The common theme: human judgment is applied during discovery to determine which processes create sufficient business value to warrant investment.

Yes, but reliability requires careful design. AI agents do not fail catastrophically like buggy code - they fail gradually through degraded quality. A badly-built agent might approve unqualified leads or miscategorise documents, causing business damage.

Our approach to reliability:

Confidence Scoring: Not all decisions have equal certainty. We implement confidence thresholds - if an agent is less than 85% confident, it escalates to a human. This ensures critical decisions have appropriate oversight.

Human-in-the-Loop Design: For high-stakes decisions (large financial commitments, customer disputes), agents prepare recommendations that humans review and approve. This combines AI speed with human judgment.

Continuous Monitoring: We track agent performance metrics in real-time - decision quality, user satisfaction, error rates, business outcomes. We do not just deploy and forget.

Guardrails & Constraints: Agents operate within defined boundaries (spending limits, approval chains, policy rules) that prevent catastrophic decisions.

Feedback Loops: Real-world outcomes feed back to improve agents over time. If an agent approves a bad lead, that signal helps retrain.

This is where judgment matters. Building reliable agents requires experience with failure modes and discipline in testing and monitoring.

Yes - and this is critical. An AI agent is only useful if it connects to your actual business systems (CRM, billing, ticketing, ERP, data warehouses). We design agents to work within your existing technology stack, not replace it.

Common Integrations:

• CRM Integration: Agents read customer history, update opportunity status, log interactions, automatically route leads.
• Ticketing & Support Systems: Agents read incoming tickets, draft responses, assign priority, escalate to humans.
• Data Warehouses & Analytics: Agents query your business data, analyse trends, generate insights, power dashboards.
• ERP & Finance Systems: Agents process invoices, generate purchase orders, reconcile accounts, flag exceptions.
• Email & Communication Platforms: Agents monitor inbound email, draft intelligent responses, coordinate team communication.
• APIs & Custom Data Sources: Agents access any system with an API - webhooks, REST endpoints, webhooks, custom integrations.

During discovery, we audit your technology stack and design agent workflows that fit naturally into your existing operations. We do not build agents in isolation - they integrate where the business value is.

Chatbots are reactive - they respond to user messages. The human initiates the conversation, and the chatbot reacts. Chatbots are useful for customer service, but they are fundamentally limited to responding.

AI Agents are proactive and autonomous. They observe systems, identify opportunities or problems, make decisions, and take action without waiting for a human prompt. An agent might automatically draft a customer response, generate a report, or escalate an issue without anyone asking.

Chatbot Examples: Customer support bots, FAQ assistants, conversational interfaces. Humans ask questions, bot answers.

Agent Examples: Autonomous lead qualification that reads CRM data and enriches leads, support agent that monitors tickets and drafts responses, operations agent that identifies billing errors and flags them for review.

Chatbots are useful for specific conversational scenarios. Agents are for automating entire business processes. For many organisations, you need both - chatbots for customer-facing interaction, agents for backend automation.

Safety and accuracy are non-negotiable in production AI agents. We build guardrails at multiple levels:

Architectural Safeguards:

• Decision Constraints: Agents operate within defined boundaries - spending limits, approval chains, policy rules. A sales agent cannot approve discounts beyond authority limits.
• Escalation Rules: Uncertain or high-stakes decisions escalate to humans. If confidence is below threshold, a human reviews.
• Audit Trails: Every agent decision is logged with reasoning, so you can understand why a decision was made and retrain if needed.

Testing & Validation:

• Test agents against real-world scenarios before production deployment.
• Measure accuracy across different customer segments and edge cases.
• Identify failure modes and add guardrails to prevent them.

Continuous Monitoring:

• Track accuracy metrics in real-time - decision quality, user satisfaction, business outcomes.
• Alert teams to performance degradation immediately.
• Analyse misclassifications to improve training data and logic.

Human Oversight:

• For critical decisions, humans review and approve agent recommendations.
• Regular audits of agent decisions to identify patterns and drift.
• Feedback loops: outcomes inform retraining and logic improvements.

This requires discipline and experience. We have built hundreds of automated systems, and that experience is what prevents disasters.