Grounded, not guessing
Retrieval-augmented generation over your own corpus, with citations, confidence signals and refusal behaviour for out-of-scope queries.
Applied Intelligence
AI that survives contact with the enterprise
Most AI pilots die between the demo and production. Infilabs closes that gap. We build retrieval-grounded assistants, agentic workflows and predictive models on top of a governed platform — with evaluation harnesses, cost controls, audit trails and human oversight designed in from the first sprint.
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AI & ML engagements delivered
0%
Average manual-effort reduction
0 wks
Typical time to first production release
Overview
From proof of concept to a governed AI operating model
We start where the value is: a specific decision, workflow or customer interaction that is expensive, slow or inconsistent today. We instrument the baseline, then engineer the smallest AI system that beats it — and we prove the delta with evaluation data rather than anecdotes.
Every solution ships on a reference architecture that separates the model from the application. Prompts, retrieval indexes, tools, guardrails and evaluation suites are versioned artefacts in your repository. When a better model arrives — and it will, roughly every quarter — you swap it in behind a stable interface instead of rewriting the product.
That discipline is what lets our clients run AI in regulated environments: financial services underwriting, clinical documentation, insurance claims and public-sector case management, with full traceability from an answer back to its source.
Evaluated continuously
Golden datasets, LLM-as-judge scoring, regression gates in CI and drift alerts in production — quality is a metric, not an opinion.
Cost-aware by design
Model routing, semantic caching, prompt compression and token budgets that keep unit economics viable at scale.
Governed end to end
PII redaction, prompt-injection defence, tenant isolation, immutable audit logs and role-scoped tool access.
What we build
Capabilities you get on day one
The components below are engineered patterns we have shipped repeatedly — not concepts we would be exploring for the first time on your project.
Retrieval-grounded assistants
Answers cite the exact clause, ticket or record they came from. Chunking, reranking and metadata filters tuned against your own evaluation set.
Agentic workflows
Task decomposition, tool calling and deterministic guardrails. Agents act on your systems through scoped, audited APIs — never raw credentials.
Safety & guardrail layer
Input/output classifiers, PII redaction, jailbreak detection, allow-listed tools and a policy engine that fails closed.
Evaluation harness
Golden datasets, rubric scoring, regression gates in CI, side-by-side model comparison and shadow deployment before cutover.
Cost & latency engineering
Semantic caching, streaming, small-model routing and batch inference — typically 40–70% below naive implementation cost.
Model-agnostic architecture
A gateway abstraction so Claude, GPT, Gemini and open-weight models are configuration, not commitment.
Capabilities
Everything inside our ai & machine learning practice
The full scope of the practice. Engagements typically draw on a focused subset — this is the bench you have access to.
Generative AI & LLM Engineering
- Generative AI strategy & use-case shaping
- LLM application development
- RAG applications & hybrid retrieval
- AI agents & multi-agent orchestration
- Prompt engineering & prompt libraries
- Fine-tuning, LoRA & instruction tuning
- Model evaluation & LLM-as-judge harnesses
- Guardrails & prompt-injection defence
Model Platforms & Integration
- OpenAI integration
- Azure OpenAI Service
- Google Gemini
- Claude AI (Anthropic)
- Amazon Bedrock
- Open-weight models (Llama, Mistral, Qwen)
- Vector databases & embedding pipelines
- Model gateways, routing & fallback
Conversational & Document AI
- AI chatbots & virtual assistants
- Conversational AI & dialogue design
- Natural language processing (NLP)
- OCR & intelligent document processing
- Document intelligence & extraction
- Speech recognition & transcription
- Voice AI & real-time speech agents
- Multilingual & translation workflows
Applied Machine Learning
- Predictive analytics & forecasting
- Recommendation engines
- Computer vision & visual inspection
- Deep learning & neural architectures
- Anomaly & fraud detection
- Churn, propensity & risk scoring
- Time-series & demand planning
- Optimisation & decision intelligence
MLOps & AI Operations
- MLOps platform engineering
- AI model deployment & serving
- Feature stores & training pipelines
- Experiment tracking & model registry
- Inference autoscaling & GPU optimisation
- Monitoring, drift & data-quality alerting
- AI automation & workflow orchestration
- AI security & red-teaming
Advisory & Enablement
- AI consulting & opportunity assessment
- Responsible AI & policy frameworks
- EU AI Act & NIST AI RMF readiness
- AI centre of excellence setup
- Build-vs-buy & vendor evaluation
- AI literacy & developer enablement
- Total cost of inference modelling
- Executive AI roadmapping
Business impact
The outcomes clients measure
Figures are medians across delivered engagements in this practice. We will baseline your own numbers during discovery rather than promise these.
62%
Less manual handling
Document-heavy processes — claims, onboarding, invoice matching, clinical coding — absorbed by AI with human review only on exceptions.
3.4×
Faster knowledge retrieval
Support and field teams find grounded answers in seconds instead of navigating wikis and PDFs.
↓ 48%
Lower cost per interaction
Tiered model routing and caching cut inference spend without measurable quality loss.
100%
Traceable decisions
Every AI-assisted output carries its prompt, model version, retrieved sources and reviewer — audit-ready by default.
Technology stack
AI & Machine Learning technology stack
Selected per engagement against your existing estate, your team's skills and total cost of ownership — never by partnership tier.
Models & APIs
- Claude (Anthropic)
- Azure OpenAI
- OpenAI
- Google Gemini
- Amazon Bedrock
- Llama
- Mistral
Frameworks
- LangChain
- LlamaIndex
- Semantic Kernel
- PyTorch
- TensorFlow
- scikit-learn
- Hugging Face
Vector & Search
- Azure AI Search
- Pinecone
- pgvector
- Elasticsearch
- Weaviate
- Qdrant
MLOps
- MLflow
- Azure ML
- Amazon SageMaker
- Vertex AI
- Kubeflow
- Weights & Biases
- DVC
Serving & Runtime
- FastAPI
- Triton
- vLLM
- Ray Serve
- Kubernetes
- AWS Lambda
- Azure Functions
Observability
- LangSmith
- OpenTelemetry
- Grafana
- Prometheus
- Evidently AI
How we deliver
How a ai & machine learning engagement runs
Six stages, each with a defined output. You can stop after any one of them and still hold something useful.
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Discover & qualify
Two-week assessment: value mapping, data readiness, risk classification and a scored shortlist of use cases with expected ROI.
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Baseline & design
Instrument the current process, assemble the evaluation dataset and design the reference architecture, guardrails and human-in-the-loop points.
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Build the thin slice
One workflow, production-shaped: real data, real auth, real logging. Measured against the baseline, not a demo script.
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Harden & govern
Security review, red-teaming, load and cost testing, DR runbooks, policy sign-off and model documentation.
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Deploy & measure
Progressive rollout behind flags, shadow traffic, live quality dashboards and rollback in one step.
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Operate & extend
Managed evaluation cadence, model upgrade path, cost review and a backlog of adjacent use cases.
Industries we serve with this practice
Engagement models
How to start with AI & Machine Learning
Three commercial shapes. Most clients begin with an assessment and move into delivery once the plan is agreed.
Fixed-price assessment
From $12,000
Two to four weeks. Produces a prioritised backlog, target architecture, risk register and a costed delivery plan you own outright.
- Named architect
- Executive readout
- No obligation to proceed
Most common
Milestone delivery
Scoped per phase
Well-bounded phases priced against agreed acceptance criteria. Suited to migrations, integrations and defined product increments.
- Acceptance-based billing
- Change control included
- Fortnightly demos
Dedicated pod
Monthly retainer
An embedded team — lead, engineers, QA — working in your sprints and tooling with US-hours overlap from our India centre.
- Scale up or down monthly
- Your definition of done
- Direct team access
Indicative ranges for planning purposes. Final pricing follows scope confirmation — we do not quote before we understand the problem.
FAQs
AI & Machine Learning — frequently asked
Will our data be used to train third-party models?
No. We deploy against enterprise API tiers and private endpoints where the provider contractually excludes your inputs and outputs from training. Where policy requires it, we run open-weight models entirely inside your own VPC or on-premise GPU estate so no data leaves your boundary.
How do you stop the model from making things up?
Three layers. Retrieval grounding restricts the model to your verified corpus and returns citations. Output validation checks claims against retrieved context and structural schemas. And an evaluation harness measures groundedness on every release so regressions are caught in CI rather than by a customer. Where confidence is low, the system is engineered to say so and escalate.
Can you work with the model we have already chosen?
Yes — and we will tell you honestly if it is the wrong fit for the workload. Our gateway architecture means the model is a configuration choice. Many clients run a frontier model for complex reasoning and a smaller, cheaper model for classification and extraction in the same product.
What does a first engagement look like commercially?
Most clients begin with a fixed-price four-week AI assessment that produces a prioritised use-case portfolio, a reference architecture and a costed delivery plan. From there we move to a time-and-materials or milestone-based build, typically 8–14 weeks to first production release.
Do we need a data lake before we start with AI?
Not for most generative AI use cases — document and knowledge workloads only need governed access to the source systems. Predictive ML is different: it needs historical, labelled, reliable data, and if that foundation is missing we will scope the data engineering work explicitly rather than pretend the model can compensate.
How do you handle regulatory exposure such as the EU AI Act?
We classify each use case by risk tier at design time, then attach the required controls: technical documentation, data-governance records, human-oversight design, accuracy and robustness testing, and logging retention. The output is an evidence pack your compliance team can file, not a slide deck.
Related practices
Data Engineering & Analytics
Lakehouse platforms, ETL/ELT pipelines and governed analytics that turn scattered systems into one trusted layer.
Cloud Services
Migration, cloud-native engineering, Kubernetes, FinOps and resilience across AWS, Azure, Google Cloud, Oracle and IBM.
Microsoft Services
Azure, Microsoft 365, Power Platform, Dynamics 365, Fabric and Copilot — delivered by a Microsoft-first practice.
Emerging Technologies
AR/VR/MR, digital twins, spatial computing, robotics, edge AI and quantum-readiness advisory.
Applied Intelligence
Ready to talk about ai & machine learning?
Send the context — current systems, constraints, what you have already tried. An architect from this practice will reply, usually within one business day.