01 — AI Engineering
We design, build and deploy custom AI around your data, workflows and goals — from autonomous agents to process automation. No off-the-shelf bots: production systems with evals, guardrails and human fallbacks from day one.
What we deliver
AI products, agents, RAG and automation shipped to production with evals and guardrails.
01
Agents that reason, plan and execute multi-step work — querying databases, calling APIs, drafting documents and coordinating with other agents — with guardrails, monitoring and human-in-the-loop fallbacks.
02
Semantic search that understands intent, recommendations that learn from behaviour, content generation and conversational interfaces — with prompt engineering, output validation, caching and fallbacks for production reliability.
03
Automation that copes when inputs vary: read emails, invoices, contracts and tickets, extract what matters, apply your business rules and act — wired into Slack, Salesforce, HubSpot or Notion, with a dashboard showing what it did.
04
Custom ML models for demand forecasting, churn prediction, lead scoring and fraud or anomaly detection. We own the whole pipeline — data cleaning, features, training, validation, deployment and monitoring — and serve models through APIs your dashboards already use.
05
End-to-end products with AI at the core: model selection, streaming responses, vector databases for RAG, evaluation frameworks, latency and cost management, usage-based billing and graceful degradation when models misbehave.
06
A two-week discovery sprint: stakeholder interviews, a data-infrastructure audit and workflow mapping to find the three to five highest-value opportunities — delivered as a prioritised roadmap with effort, impact and build-vs-buy calls.
How we work
01
We audit your workflows, data and goals to find the AI opportunities with the highest impact, and agree how success will be measured.
02
In two to three weeks we build a working proof-of-concept on your real data — not a slide deck — to validate the approach before you commit further.
03
Production engineering with tests, evals, guardrails, monitoring and human fallbacks for uncertain outputs.
04
Launch, watch accuracy and usage on a live dashboard, gather feedback and keep improving the system with each release.
Engagements & proof
Typical engagement
Most AI work starts with a two-to-three-week proof-of-concept on your real data, then moves to a six-to-eight-week production build once the approach is validated.
Fixed-price packages for well-defined scope; hourly or retainer for evolving work. Every quote follows a scoping call.
See packages & pricing75%Target share of tickets resolved without a human
E-Commerce · 4 Weeks
How we deploy an AI support agent that handles order queries, returns and FAQs autonomously.
1Publish path, shared by people and the agent
Social Media Management · Built August 2026
How we let an AI agent commit content inside SocialHub while keeping every human control that hand-written posts already had.
FAQ
Something else? Ask us directly — we reply within 24 hours.
A proof-of-concept typically takes two to three weeks. Production systems take six to twelve weeks depending on complexity, data readiness and integrations. We always start with a focused prototype to validate the approach before a full build.
Not always. Many solutions work with pre-trained models such as Claude or GPT that need no custom training data. Predictive analytics and personalisation do need historical data — we assess what you have and identify gaps during discovery.
Data stays within your infrastructure or approved cloud environments. We support private model hosting and on-premise deployments, work within your compliance requirements (for example GDPR or HIPAA), and never use your data to train models without explicit consent.
We are model-agnostic and pick the right tool for each use case: Anthropic Claude, OpenAI, open-source models such as Llama and Mistral, and specialised models for vision, speech and embeddings. For agents we use the Claude Agent SDK, LangChain, CrewAI or a custom orchestration layer.
Every production system ships with guardrails: output validation, confidence scoring, human-in-the-loop fallbacks and monitoring. When the model is uncertain it escalates to a person instead of guessing, and evaluation pipelines measure and improve accuracy over time.
Yes — most of our AI work extends existing products and workflows. We integrate through APIs, webhooks and native SDKs with platforms such as Salesforce, HubSpot, Slack, Notion, Shopify and custom backends. Nothing needs rebuilding from scratch.
Next step
Tell us what you want to build. In a 30-minute session we map the scope, the risks and the fastest route to something running.
We reply within 24h · hello@tabncode.com