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Home / Services / AI & Automation

AI and
AUTO
MATION

Service Category
Intelligent Systems
Domains
LLM · ML · RPA · BPA
Typical Engagement
1 – 9 months

We deploy intelligent automation systems that eliminate manual workflows, surface hidden intelligence in your data, and let your team focus on the work that actually requires humans.

Production-Grade AI, Not Demos We go beyond proof-of-concept. Every model we ship has an evaluation framework, a monitoring harness, and a rollback strategy — because production AI behaves differently than sandbox AI.
LLM Integration Done Right From RAG pipelines to fine-tuned domain models, we architect LLM systems with latency budgets, cost ceilings, and accuracy benchmarks agreed before the first API call is made.
Automation That Compounds Each automated workflow frees capacity for the next. We design automation roadmaps — not one-off scripts — so your operational efficiency compounds quarter over quarter.
What's included

WHAT WE
DELIVER

LLM Application Development

Custom RAG pipelines, AI agents, and LLM-powered features integrated directly into your product stack. We handle prompt engineering, context management, and evaluation frameworks end to end.

Business Process Automation

End-to-end workflow automation across your tools — CRM, ERP, communication platforms, and internal systems connected into seamless, auditable pipelines that run without human intervention.

ML Model Development

Custom machine learning models for classification, regression, anomaly detection, and recommendation. From data preparation through training, evaluation, and production serving infrastructure.

Predictive Analytics

Demand forecasting, churn prediction, pricing optimisation, and risk scoring models built on your historical data. We deliver explainable outputs your business stakeholders can act on with confidence.

Intelligent Document Processing

OCR, entity extraction, classification, and structured data extraction from unstructured documents — invoices, contracts, forms, and reports processed at scale with human-review fallback loops.

AI Ops & Model Monitoring

Drift detection, accuracy tracking, data quality monitoring, and automated retraining pipelines that keep your models performing in production — long after the initial deployment excitement fades.

Tools & platforms

OUR
STACK

LLM
OpenAI / Anthropic
LLM
LangChain / LlamaIndex
ML
PyTorch
ML
scikit-learn
Vector DB
Pinecone / Weaviate
Orchestration
Prefect / Airflow
Serving
Ray Serve
Automation
n8n / Zapier
Monitoring
Weights & Biases
OCR
Textract / Tesseract
Experiment
MLflow
Data
Spark / Dask
Featured work

CASE
STUDY

Doc Ingestion
Chunker
Embedder
Vector Store
LLM
Response
AI / Legal Tech
Bizett Contract Intelligence Platform

Bizett's legal team was spending 60% of their time manually reviewing contracts. We built a RAG-powered intelligence layer over their document repository that extracts key clauses, flags risk, and answers natural-language queries against a corpus of 400,000+ contracts.

73%
Review Time Saved
400K+
Contracts Indexed
94%
Extraction Accuracy
Common questions

FAQ

How do you evaluate whether AI is the right solution? +
We start every engagement with a feasibility assessment — analysing your data, the task complexity, and the cost-benefit of AI versus simpler automation. We'll tell you honestly if a rules engine or a SQL query will serve you better than a language model.
What data do we need to get started? +
It depends on the use case. For LLM applications, you often need very little labelled data. For custom ML models, we typically need 6–24 months of historical operational data. We'll scope exact data requirements during the discovery phase.
How do you handle model accuracy and hallucinations? +
We design evaluation suites before building, establish accuracy baselines, and implement grounding strategies — retrieval augmentation, structured output enforcement, and confidence scoring — to keep model outputs within acceptable bounds. Human-in-the-loop fallbacks are built in wherever stakes are high.
Will our data be used to train third-party models? +
No. We use enterprise API tiers with data processing agreements that explicitly prohibit training on customer data. For highly sensitive domains, we can architect fully on-premises or VPC-isolated deployments using open-source models.
How do you manage AI costs at scale? +
We implement caching layers, prompt compression, model routing (using smaller models for simpler tasks), and per-request cost tracking from day one. Every system we build has a cost dashboard so you know exactly what each feature is spending.
Ready to build

START YOUR
PROJECT

Tell us about the workflows you want to automate and we'll map a path from where you are to where you want to be.

Reach Out →