We build production-ready AI products, and bring autonomous agents into the software your business already runs on.
“Can I move my appointment to Friday afternoon?”
Launching an AI-first product or modernising decade-old software, we bring the agentic architecture and the engineering to reach production.
From first architecture decision to production.
We turn a concept into an AI-native product with foundations that scale, ready for real users from launch day.
Add intelligence. Keep the systems that work.
Skip the 12-month rewrite. We add agentic layers on top of your databases, monoliths and portals, and leave the core untouched.
Agents that do the work, and show their work.
Specialised agents plan, execute, verify and self-correct across your CRM, ERP, finance and support tools.
Voice AI built for real conversations.
Voice agents on Tunk's own engine that answer and place calls with natural turn-taking, and update your CRM while the call is live.
Every business has different constraints. Pick yours to see the engagement, the timeline and what you walk away with.
Specialised AI engineers are slow and costly to hire, and early architectures often fail to scale or run up LLM bills.
We act as your AI engineering team: architect, build, test and ship, with latency, caching and model routing tuned from day one.
Days 1–7
Scoping, UX wireframes and architecture
Days 8–14
Core agent engine and a working end-to-end POC
Days 15–28
Full UI, auth, billing and tenant provisioning
Days 29–42
Load testing, hardening, observability, launch
Weekly sprint reviews · shared Slack channel · full IP transfer
No theatre and no open-ended discovery. A small senior team moves from a real problem to a real system, with a clear deliverable at the end of every phase.
We audit workflows, inspect schemas and set latency budgets. By day 14 you have a working end-to-end prototype on staging.
Live POC and architecture specMulti-agent loops, guardrails, database connectors and human-in-the-loop approval gateways, built and tested.
Hardened staging and automated test suiteWe deploy into your cloud, transfer 100% of the IP and train your team, with a dedicated pod available after launch.
Production release and full source repositoryHow a specialised agentic AI team compares with a traditional agency or an in-house effort.
Enterprise AI cannot run on black-box assumptions. We design around your data, your infrastructure and your risk profile from the first architecture conversation.
Discuss your requirementsPrivate VPC or on-prem
Your AWS, Azure or GCP account, with KMS keys and private subnets.
Zero data retention
Prompts and outputs never train models; PII is scrubbed automatically.
Deterministic guardrails
Schema validation, critic agents and prompt-injection shields.
Human-in-the-loop
High-impact actions wait for one-click approval, with full rollback.
SOC 2 & HIPAA patterns
TLS 1.3, AES-256 at rest, RBAC and immutable audit trails.
Most agencies build web apps and lack depth in agent state machines, real-time voice and retrieval. Hiring in-house takes 3 to 6 months before any code is written. We bring a senior AI team that ships a working POC in 14 days and a production system in 4 to 8 weeks.
Our first phase delivers a working, end-to-end prototype connected to your data within 14 days. Full products, legacy retrofits and voice rollouts typically take 4 to 8 weeks.
Yes, 100%. Source code, repositories, infrastructure scripts, prompts, agent graphs and evaluation datasets all belong to you. No proprietary runtime, seat licences or black-box hosting.
Yes, it is one of our core specialisations. We wrap your databases, APIs and internal portals with an agentic layer, using MCP and a strangler-fig approach, so workflows get automated without destabilising the core.
Yes. We deploy into your AWS, Azure or GCP VPC with private networking and your own encryption keys. For sensitive workloads we can run open-weight models fully isolated from the public internet.
We use deterministic state machines, strict schema validation and critic agents that verify output. Actions that change data, such as a refund or a record update, pause for human approval over Slack or a webhook.
Two paths: take full ownership with documentation and team training, or keep a dedicated AI pod for evaluation, cost optimisation, latency tuning and new features.
Bring us the hard problem, the half-built idea, or the system that needs a smarter layer.
