Autonomous AI that takes action
Move beyond chatbots that just answer questions. We build goal-driven AI agents that reason, plan, and execute real business workflows — from qualifying leads and booking meetings to processing refunds and resolving support tickets end-to-end.
Agentic AI represents a fundamental shift from retrieval-based chatbots to autonomous systems that can complete multi-step tasks. According to Gartner, by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, and 15% of day-to-day work decisions will be made autonomously through agentic AI. McKinsey estimates generative AI and agentic systems could add $2.6–4.4 trillion in annual value across industries. We build production-grade agents on Claude, GPT-4, Gemini, and open-source models — with the observability, guardrails, and tooling needed to run them safely in production.
33%
of enterprise apps will include agentic AI by 2028 (up from <1% in 2024)
$4.4T
estimated annual value from generative and agentic AI
79%
of organisations have adopted generative AI in at least one business function
Core capabilities
Customer-facing agents
Support, sales, and booking agents that handle tier-1 queries, qualify prospects, and route complex issues — 24/7, in any language.
Voice AI agents
Inbound and outbound phone agents built on Vapi, Retell, or ElevenLabs for reception, appointment booking, surveys, and cold outreach.
RAG-powered assistants
Retrieval-augmented chatbots grounded in your own documents, knowledge bases, and product data — no hallucinations, full source attribution.
Internal productivity agents
Team-chat bots that summarise threads, draft emails, pull data from tools, and automate the repetitive work your team hates.
Multi-agent systems
Specialist agents (researcher + writer + reviewer) that collaborate to handle complex workflows humans currently juggle across tools.
Tool-using agents
Agents with function-calling access to your CRM, ERP, calendar, payment, and custom APIs — actually doing the work, not just describing it.
What separates an AI agent from a chatbot?
A traditional chatbot matches patterns and returns pre-written responses. An AI agent has a goal, plans a sequence of actions, uses tools (APIs, databases, external services), observes the results, and adjusts until the goal is complete. IBM defines agentic AI as systems that can pursue complex goals and workflows with limited direct human supervision, exhibiting autonomy, reasoning, and tool use. In practice, this means the difference between a bot that says "I'll connect you to billing" and one that actually issues the refund, updates your CRM, sends the confirmation email, and closes the ticket.
Where agentic AI delivers measurable ROI
- Customer support: resolve 40–70% of tier-1 tickets without human intervention, reducing cost per contact and improving response times.
- Sales qualification: automated 24/7 lead qualification and meeting booking — every inbound lead gets a response within seconds, not hours.
- Appointment scheduling: voice agents handle phone bookings, rescheduling, and reminders at a fraction of the cost of live reception.
- Back-office operations: invoice processing, data entry, and document classification handled autonomously with human-in-the-loop for edge cases.
- Internal knowledge search: employees query company documentation in natural language and get cited, verifiable answers in seconds.
Our technology stack
We choose the right model and framework for each use case rather than forcing one tool into every problem. For reasoning-heavy agents we default to leading frontier models. For cost-sensitive high-volume workloads we use smaller frontier models or fine-tuned open-source alternatives. For orchestration we use industry-standard agent frameworks or custom services depending on complexity. For RAG we use leading managed or self-hosted vector databases. For voice we use leading voice-AI platforms. Every agent ships with observability, evaluation suites, and cost dashboards.
Why trust matters more than capability
The hardest part of deploying an AI agent isn’t getting it to work — it’s getting it to work safely. We build every agent with explicit scope boundaries, tool-call authorisation, human-in-the-loop escalation, audit trails, and continuous evaluation. We red-team prompts against jailbreaking, PII leakage, and policy violations before launch. Published research on agentic misalignment shows that even frontier models can exhibit harmful behaviours under pressure, which is why guardrails are not optional.
Real-world use cases
E-commerce
A mid-size retailer deployed a support agent handling returns, order lookups, and product recommendations.
Outcome: 63% of tickets resolved without human agents, average response time dropped from 4 hours to 8 seconds, CSAT up 12 points.
Professional services
A law firm deployed a voice agent for inbound new-client enquiries, handling qualification and calendar booking.
Outcome: Captured 100% of after-hours calls (previously voicemail), booked 3x more consultations per month without additional staff.
SaaS
A B2B SaaS company built a RAG assistant grounded in their help centre, API docs, and helpdesk ticket history.
Outcome: Cut support ticket volume by 41%, reduced time-to-first-response to under 5 seconds, freed senior engineers from answering repeat questions.
Healthcare
A dental group deployed an appointment-booking voice agent integrated with their practice management system.
Outcome: Handled 78% of inbound booking calls autonomously, captured bookings outside business hours, no-show rate dropped 18%.
Our delivery process
- 1
Discovery & scoping (1 week)
We map your workflows, identify the highest-ROI agent use case, define success metrics, and confirm data and integration requirements.
- 2
Prototype & evaluation (2–3 weeks)
We build a working prototype against a curated evaluation set, benchmark model choices, and demonstrate real task completion — not just demo-quality conversations.
- 3
Integration & guardrails (3–5 weeks)
We integrate with your CRM, helpdesk, calendar, payment, and internal APIs. We add authorisation, audit logging, escalation paths, and red-team the agent against jailbreaking and PII leakage.
- 4
Launch & monitoring (ongoing)
We deploy with staged rollout (internal → beta users → full production), monitor completion rate, cost per conversation, and CSAT, and iterate weekly against real production traffic.
What you get
Frequently asked questions
How is an AI agent different from a ChatGPT integration?
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A ChatGPT integration returns text. An AI agent plans a sequence of actions, calls your APIs, reads and writes to your databases, and completes the task end-to-end. The agent architecture includes tool use, memory, reasoning loops, and guardrails — none of which come out of the box with a raw model API.
Which model do you recommend?
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It depends. For reasoning-heavy tasks (legal, medical, complex support) we use the strongest available frontier models. For high-volume cost-sensitive tasks (tier-1 support, classification) we use smaller frontier models or fine-tuned open-source alternatives. We benchmark options during the prototype phase and choose based on your cost-per-task budget and accuracy requirements.
How do you prevent hallucinations?
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Three layers: (1) RAG grounding so the agent only answers from your verified sources, (2) structured output validation that rejects malformed responses, (3) evaluation suites that test against known ground truth and catch regressions before they hit production. We cite every answer back to its source document.
What does a typical engagement cost?
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Prototype engagements start at $8,000–15,000 for a 3–4 week proof of value. Production agents typically range $25,000–80,000 depending on integration complexity, guardrail requirements, and volume. Monthly running costs depend on model choice and traffic — most clients run production agents for $200–2,000/month in inference costs.
How do we measure success?
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We define success metrics upfront — typically task completion rate, human handoff rate, cost per resolved interaction, CSAT, and business-specific KPIs (bookings captured, revenue influenced, tickets deflected). You see these live in the observability dashboard we build with every engagement.
Do you build on frontier or open-source models?
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Both, depending on requirements. For data-sensitive or regulated workloads we often recommend frontier models with strong safety track records, or self-hosted open-source alternatives. For rapid iteration and broad capability, we use leading managed frontier APIs. We are model-agnostic and will recommend what fits your constraints.