AI & Process Automation
LLM integrations, RAG copilots, and agentic workflow automation, grounded in your data, evaluated against held-out sets, and built to compound value with every interaction.
Most AI projects fail in the gap between demo and production.
The demo works because it is curated. Production does not work because it is real. Users ask unexpected questions, reference documents the model was never shown, and escalate to a human when the AI gives confidently wrong answers. We build the eval infrastructure, retrieval grounding, fallback paths, and confidence calibration that makes AI behave reliably under traffic, and the observability that catches drift before users notice.
AI only matters when it changes a workflow someone actually runs. We build production-grade AI systems, RAG copilots, agent pipelines, and automations, that ground every answer in your real data, get scored against a held-out evaluation set before each release, and improve with use. We've shipped the AI moderation, daily-summary, and unbiased-opinion features inside Daily8 (UAE news aggregator), the qualitative-analytics engine inside WholeSum, and the OctoLabs support copilot that deflects 47% of tickets, and we know the difference between a demo and a production system.
Capabilities
Custom RAG copilots
LLM-powered assistants grounded in your data, documents, tickets, CRM, knowledge base, with cited answers, not hallucinated ones. We design the chunking, embedding, and retrieval strategy specifically for your corpus, then prove it on an eval set.
Agentic workflow automation
Multi-step agents that take scoped, auditable actions on your behalf, calling tools, writing to systems, escalating to humans on confidence thresholds. Built on LangGraph or first-party SDKs (Anthropic, OpenAI), with full state persistence and replay.
RAG pipelines that actually work
Retrieval-augmented generation tuned for production: chunking strategy, embedding choice, hybrid retrieval (semantic + keyword), reranking, and the held-out eval set that proves it.
LLM evaluation harnesses
RAGAS, Braintrust, or custom eval frameworks that score every release against a representative prompt set. You get a number, not a vibe, before you ship, and gates in CI block regressions.
Process mining and automation discovery
Workflow analysis to identify the highest-leverage automation opportunities in your operations, before writing any code. Cuts wasted prototype effort dramatically.
Fine-tuning and preference optimisation
When a base model isn't enough, structured fine-tuning on your domain data (LoRA, full fine-tune), DPO/RLHF for preference alignment, and the evaluation harness to prove it worked.
How we build it
Tools and technologies we use in this practice, chosen for fit, not familiarity.
Our process
Consistent across every engagement, adapted to your constraints, not the other way around.
Use-case scoping and eval design
We define success before building anything. What does 'good' look like? How do we measure it? Output: a held-out evaluation set that doubles as the specification, every model, prompt, or pipeline change is scored against it.
Prototype, evaluate, iterate
Two-week cycles: build a narrow version, run it against the eval set, measure, and iterate. We show you the score at every review, and the failure cases that drive the next sprint.
Production hardening and operate
Confidence thresholds, fallback paths, audit logging, eval CI, token-cost dashboards, and the drift monitoring that tells you when output quality degrades. We do not ship without these, and we can stay on to operate the system if you'd rather your team not learn it overnight.
Questions teams ask before they start
What is AI process automation?
AI process automation uses artificial intelligence — including machine learning, natural language processing, and LLM-based agents — to execute, optimise, and monitor business workflows without constant human intervention. Unlike traditional RPA, AI automation can handle unstructured data, make contextual decisions, and adapt to process variations rather than breaking on edge cases.
How quickly can businesses see ROI from AI process automation?
Most SME automation projects reach payback within 6–18 months, depending on the volume of work automated and the cost of manual effort replaced. Quick-win automations — such as invoice processing or report generation — can show positive ROI within the first quarter. 7code always models expected ROI before project start so clients have a clear business case.
What are the most common AI process automation use cases?
The most in-demand use cases include invoice and document processing, customer query triage and response, HR onboarding workflows, compliance monitoring, financial reporting, and supply chain exception management. 7code has delivered automation projects across healthcare, finance, energy, logistics, and HR — adapting each solution to the client’s existing systems and compliance requirements.
Which industries benefit most from AI process automation?
Finance and insurance benefit from automated compliance checks and report generation. Healthcare gains from clinical documentation and patient triage automation. Energy companies automate meter data processing and anomaly detection. HR teams use AI to screen candidates and automate onboarding. Operations-heavy businesses in logistics and manufacturing automate exception handling and supplier communication.
How does 7code integrate AI automation with existing business systems?
7code maps existing workflows first, then designs automation layers that connect to your current tools — ERP, CRM, HRIS, or custom databases — via APIs, webhooks, or direct database connectors. We prioritise non-disruptive integration: automation runs alongside existing systems initially, with migration to automated workflows happening in controlled phases to minimise operational risk.
How is AI process automation different from traditional RPA?
Traditional RPA is rule-based and brittle — it breaks when document formats or process steps change. AI process automation uses machine learning and LLMs to interpret context, handle exceptions, and improve over time. It can process unstructured inputs like emails, PDFs, and voice, making it applicable to a far wider range of business processes.
Who maintains AI automation systems after go-live?
7code offers post-launch maintenance retainers covering model monitoring, retraining triggers, integration upkeep, and performance reporting. Clients can also opt for a knowledge-transfer model where 7code trains the in-house team to own operations. All automations are built with observability baked in — dashboards and alerting are standard, not extras.
How do I get started with AI process automation at 7code?
The first step is a free 30-minute scoping call with a 7code senior consultant. We identify two to three high-value automation opportunities, provide a rough ROI estimate, and outline a proposed approach. If there’s a fit, we formalise scope and begin a paid Discovery Sprint — typically two weeks — before committing to a full build.
Projects using this service
Ready to build your next product?
Tell us about your project. We'll respond within one business day with next steps.
We use cookies
We use essential cookies for the site to work, and analytics cookies (Google Analytics) to understand how you use it. Cookie Policy.