Practice · 03 of 06
AI, Automation & Data
Work that a machine should be doing, done by a machine — and the data underneath it made trustworthy first.
An AI project on bad data produces confident nonsense. These three belong on one page because they are one job: get the data right, automate the routine, then let a model do the part that needs judgement.
- Services
- 35
- Categories
- 3
Every service on this site is delivered by senior developers with ten to fifteen years in that niche. Whatever the brief asks for, it gets built.
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01
AI customer-support chatbot
A website or WhatsApp bot that handles tier-one questions itself.
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02
RAG knowledge assistant
An assistant trained on company documents — accurate, and it cites.
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03
AI agent / multi-agent workflow
Autonomous agents running a whole business process.
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04
LLM integration into a product
AI features inside an existing app — summarise, draft, classify.
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05
Fine-tuning & model customisation
Domain-specific model training, with evaluation.
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06
Prompt engineering & prompt library
Reusable, tested prompts and the guardrails around them.
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07
AI voice agent
A voice bot that handles inbound or outbound calls.
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08
Computer vision solution
Detection, OCR, quality control and counting.
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09
Recommendation engine
Personalised product or content suggestions.
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10
Predictive analytics & forecasting
Demand, churn and revenue forecasting models.
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11
Document processing & extraction
Structured data pulled out of invoices, contracts and forms.
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12
AI content generation pipeline
Bulk content in a brand voice, with review and QC steps.
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13
AI SDR / lead qualification bot
Leads researched, scored and approached, automatically.
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14
MLOps & model deployment
Serving, versioning, monitoring and drift alerts.
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15
AI readiness & strategy audit
Where AI belongs and where it does not — ranked by return.
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16
AI governance & compliance
Policy, risk register, and the EU AI Act and data rules.
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17
Synthetic media production
AI avatars, voice clones and localised video at scale.
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01
Workflow automation (Make / n8n / Zapier)
Manual tasks turned into connected automations.
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02
CRM automation & pipeline setup
Stages, triggers, follow-ups and reporting — the sales engine.
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03
Robotic process automation (RPA)
Bots driving legacy software through repetitive work.
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04
Internal tools (Retool / Airtable / Notion)
Custom dashboards and ops apps without a full build.
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05
Scraping & monitoring pipelines
Competitor prices, listings and leads on a schedule.
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06
WhatsApp / Telegram bots
Booking, orders, support and notifications inside chat.
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07
Document & reporting automation
Proposals, invoices and weekly reports that build themselves.
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08
Ops audit → automation roadmap
Where the team’s hours go, what can be automated, in order.
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01
Analytics implementation (GA4 / GTM)
Events, goals and e-commerce tracking — data worth trusting.
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02
BI dashboards
Looker, Power BI or Metabase — views you can decide from.
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03
Data warehouse setup
A single source of truth in BigQuery or Snowflake.
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04
ETL / data pipelines
Data pulled from systems, cleaned, and scheduled.
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05
Data cleaning & migration
Duplicates, formats and legacy migrations put right.
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06
Customer data platform setup
Unified customer profiles across tools and channels.
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07
Attribution modelling
Which channel is actually bringing the revenue.
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08
Automated KPI reporting
Weekly or monthly packs that build and deliver themselves.
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09
Data strategy & governance
Ownership, quality rules, access and retention policy.
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10
Survey & market research analysis
Collection, statistical analysis and an insight report.
Not sure which of these you need?
Start with the audit. It measures what is already there and says plainly what it could not check — which is usually the shortest route to knowing what is worth building.