Turn scattered data and AI ambition into models, pipelines, and decisions you can trust in production.
AI & Data Services
Harness the power of artificial intelligence and data analytics to transform your business operations and decision-making.
The challenge
Most organizations are sitting on data they can't act on and AI pilots that never make it past the demo. Data is fragmented across SaaS tools, warehouses, and spreadsheets; "insights" arrive as stale reports nobody trusts; and generative AI experiments stall on hallucinations, unclear ROI, governance worries, and no path to production. The result is a widening gap between what leadership expects from AI and what the engineering org can actually ship and operate safely.
How we help
We treat AI and data as one engineering discipline, not two disconnected projects: we build the data foundation (clean, governed pipelines and a single source of truth) and the intelligence layer (ML models, RAG-grounded GenAI, and analytics) on top of it. Our own products accelerate the work — BotManor provides production RAG pipelines, multi-LLM orchestration, and agent tooling so generative features are grounded and observable; BigConsole turns the resulting data into role-specific dashboards and predictive views without rebuilding BI from scratch; and FluidGrids orchestrates the ingestion, transformation, and retraining workflows that keep everything fresh. Because we build and operate these platforms ourselves, we ship to production with evals, guardrails, and monitoring from day one rather than leaving you with a prototype.
New: Data You Can Trust, explained in 2 min 9 sec
Know where your numbers came from, and who looks after them.
Data engineering and governance means getting your numbers from where they start to the people who use them: moved the same way each time, checked on the way, labelled with where they came from, and looked after by someone named.
New: Knowledge You Can Find, explained in 2 min 19 sec
Help your people find what your company already knows.
Knowledge and semantic engineering means connecting what your organisation already knows and agreeing what its words mean, so people can find an answer without knowing which shelf, folder or person it lives with.
What you get
- A data architecture and AI readiness assessment with a prioritized, ROI-ranked use-case roadmap
- Production data engineering pipelines (ingestion, transformation, quality checks, orchestration) for batch and streaming sources
- Trained and validated ML/predictive models with documented evaluation, versioning, and a deployment path
- Grounded generative-AI features built on RAG with retrieval, semantic chunking, and multi-LLM orchestration (BotManor)
- Role-specific BI and analytics dashboards with real-time and predictive views (BigConsole)
- An evaluation, guardrail, and observability harness covering model quality, drift, cost, and safety
- Documentation, runbooks, and team enablement so your engineers can own and extend the system
How we engage
Discover & assess
We audit your data sources, quality, governance, and existing AI efforts, then map candidate use cases against business value and feasibility to agree on what ships first.
Foundation & architecture
We design the data architecture and build the pipelines, storage, and access layer that everything downstream depends on — with quality checks and lineage baked in.
Build & validate
We develop the models, RAG/GenAI features, and analytics, validating each against held-out data and task-specific evals so accuracy, cost, and safety are measured, not assumed.
Productionize & integrate
We deploy into your environment with orchestration (FluidGrids), monitoring, and guardrails, wiring outputs into the dashboards (BigConsole) and apps where decisions actually happen.
Operate & enable
We monitor drift and quality, retrain on a cadence, and hand over documentation and training so your team can run and evolve the system independently.
Outcomes you can expect
- Decisions backed by a trusted single source of truth instead of conflicting reports and manual spreadsheet wrangling
- Generative-AI features that reach production with grounding, evals, and guardrails rather than stalling as demos
- Faster, more reliable analytics through automated pipelines that remove brittle manual data prep
- Reduced model and AI operating risk via monitoring for drift, quality, cost, and safety
- Lower long-term cost of ownership because your team is enabled to operate and extend the system
- A clear, prioritized roadmap that ties each AI and data investment to a business outcome
Powered by our products
We deliver this faster and more reliably with the Algoshred product suite.
Frequently asked questions
No. A common first engagement is building or cleaning up the data foundation precisely because the AI use case exposed gaps. We can start with the assessment, stand up pipelines and a single source of truth, and layer models and GenAI on top as the data matures — or work within the warehouse and tools you already have.
Where they genuinely accelerate delivery we build on BotManor (RAG and multi-LLM orchestration), BigConsole (dashboards and analytics), and FluidGrids (pipeline and retraining orchestration). They let us reach production faster instead of rebuilding plumbing. They're optional — we also deliver on your existing stack and cloud, and we're upfront about where a product adds value versus where it doesn't.
You own the data, the trained models, the pipelines, and the application code we write for you, along with the documentation and runbooks. If a deliverable runs on one of our products, that platform stays licensed, but everything custom-built for your use case is yours.
We ground GenAI in your own data using RAG rather than relying on a model's open-ended memory, and we add task-specific evals, guardrails, and human-in-the-loop checkpoints where stakes are high. Data handling, access controls, and retention are designed around your governance and compliance requirements from the start.
Most engagements open with a short discovery and assessment phase to align on the highest-value use case, followed by iterative build phases that ship a working slice to production early and expand from there. We scope to a concrete first outcome rather than an open-ended program, and timeline depends on data readiness and integration complexity — which the assessment is designed to surface upfront.
Yes. We frequently embed with in-house teams to fill specific gaps — data engineering, MLOps, evals, or GenAI productionization — and transfer knowledge as we go. The enablement and documentation deliverables exist so your team can own and extend the system after we step back.
Ready to get started?
Let's discuss how AI & Data Services can help solve your problem and move your business forward.
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