AI development services, from
data foundations to production agents.
Custom LLM fine-tuning, AI agents and automation, enterprise data platforms on Databricks and Snowflake, and forward deployed engineers who ship production systems. Built for startups and enterprises worldwide, and handed over with the documentation your team needs to run them.
Seventeen services in five groups, one rule: every one ships to production with the documentation your team needs to run it.
Arrive with the problem. We will map it to the service.
Most people do not come to us asking for a service by name. They come with one of these four situations. Pick the one that sounds like yours.
If this sounds like you
“Our data is not ready for AI yet”
The ambition is clear but the information sits across a dozen systems that were never designed to agree. We build the platform first, on Databricks, Snowflake, or your own cloud, and the intelligence on top of it second.
Data platforms and enterprise AIIf this sounds like you
“Our pilot stalled at the security review”
The demo worked on sample data and stopped moving the moment it met residency rules, entitlements, and a compliance function that needs an audit trail. We design around those constraints from the first conversation.
Enterprise AIIf this sounds like you
“A process quietly eats our week”
Documents in shifting formats, queues someone has to triage, reconciliations that back up, reports rebuilt every Monday. We automate the recurring work with a mechanical check before anything counts and a person approving anything irreversible.
AI automationIf this sounds like you
“We need senior capacity, not another project”
Recruiting a senior AI engineer takes six to nine months. We embed one in your team in about two weeks, working in your repos against your real data, and you meet them before you commit.
Forward deployed engineersOur flagship, embedded engineering
Forward Deployed Engineers
Hire a senior FDE who joins your standups, builds in your codebase, and ships production AI on your stack. PoC in 2 to 4 weeks. Production in 8 to 14 weeks. Knowledge transfer is built in from the first week.
- Senior engineer (or pod) embedded in your team
- PoC against your real data in 2 to 4 weeks
- Production system in 8 to 14 weeks
- Full source-code handover at completion
Data platforms and enterprise AI
Most enterprise AI stalls on the data underneath it, so we build the platform first and the intelligence second. Governed lakehouses and warehouses, inference inside your own cloud, and the residency, audit, and access controls your security team already expects.
Lakehouse AI on Databricks
Databricks Intelligence
Three production systems on your Databricks environment: secure document intelligence with Unity Catalog PII scrubbing at ingestion, conversational analytics through Genie Spaces mapped to your business vocabulary, and multi-agent workflow automation on Mosaic AI. Governed by Unity Catalog and traced with MLflow.
What is included
- Medallion architecture and Unity Catalog governance
- Document intelligence with Mosaic AI Vector Search
- Genie Spaces configured to your KPI definitions
- Multi-agent workflows with MLflow tracing
Cortex Search and Cortex Analyst
Snowflake Intelligence
Two intelligence layers on the Snowflake you already run. Document Intelligence makes Confluence, Jira, PDFs, CRM notes, and email semantically searchable with cited answers. Natural Language Analytics turns English questions into schema-aware SQL with written explanations. No migration, no new vendor, full source at handover.
What is included
- Document ingestion and Cortex Search retrieval
- Natural-language-to-SQL with Cortex Analyst
- Existing role-based access and masking inherited
- Admin page for cost, latency, and feedback
Passes the security review
Enterprise AI
AI designed around the constraints that actually decide whether it reaches production: data residency, compliance evidence, existing identity and entitlements, legacy system integration, model governance, and no vendor lock-in. Security review happens before the build, not after it.
What is included
- Inference inside your own cloud account
- Audit trail as a by-product of every run
- Mainframe, ERP, and batch interface integration
- Weights, pipelines and runbooks handed over with documentation
AI Data Foundations
Data engineering and pipelines
The ingestion, modelling, and pipeline work that every AI system depends on and most projects underestimate. Warehouse and lakehouse modelling, vector search infrastructure, streaming ingestion, and the observability that keeps data trustworthy once the model is live.
What is included
- Ingestion pipelines from files, APIs, and legacy systems
- Warehouse and lakehouse modelling
- Vector search and embedding infrastructure
- Data quality monitoring and pipeline repair
Custom models and LLM development
We build AI from the model level up. Whether you need a fine-tuned language model, a speech pipeline, or a domain-specific ML system, we design and train it on your data so it performs on your tasks, not just on benchmarks.
Custom LLM Development
LLM training and fine-tuning
We train and fine-tune large language models on your proprietary data, giving you AI that understands your domain, your terminology and your specific business context. The result is a model built for your domain, not a general one you rent.
What is included
- Custom dataset curation and preparation
- Model selection and fine-tuning
- Evaluation, benchmarking and safety testing
- Production deployment and monitoring
Custom ML Algorithms
Machine learning
We build custom machine learning algorithms from the ground up. Whether you need predictive models, classification systems or recommendation engines, we design ML solutions built specifically for your use case and data.
What is included
- Algorithm design and architecture
- Model training and validation
- Performance optimisation
- Integration with your existing systems
Voice AI Development
Custom speech-to-speech pipelines
End-to-end speech AI covering recognition, language understanding, response generation and voice synthesis. Built for voice assistants, call automation and real-time communication products.
What is included
- Automatic speech recognition (ASR)
- Natural language understanding (NLU)
- Text-to-speech synthesis (TTS)
- Real-time pipeline & latency optimisation
Anthropic Claude Integration
AI enablement with Claude
Production-ready AI applications powered by Anthropic Claude. From document analysis to code generation and customer-facing AI tools, we integrate Claude deeply into your product and internal workflows; through custom Skills, MCP servers, and context engineering.
What is included
- Custom Claude Skills development
- MCP servers for your systems
- Context engineering & prompt registry
- Eval harness, red-team & runbooks
AI agents and automation
AI systems that reason, plan and act. From agents that hold real tools and complete multi-step work, to automation that absorbs the recurring operational load, to conversations that end in a finished action rather than an explanation.
Agentic AI Development
AI agent development
Custom agents that reason over your data, call the tools your team already uses, and carry multi-step work through to completion. Single-task agents, multi-agent systems, MCP and tool integration, all gated by evaluation harnesses and human approval before anything irreversible.
What is included
- Agent architecture & reasoning design
- Tool, API and MCP integration
- Evaluation harness from your real cases
- Scoped permissions and approval gates
Intelligent Process Automation
AI automation
Automation for the work that quietly eats the week: document intake, triage and routing, reconciliation, reporting, and the integrations that keep breaking. Built with mechanical verification and a human approving anything that matters, delivered assisted first and widened as the evidence justifies.
What is included
- Discovery scored against four automation tests
- Verification layer built before the automation
- Assisted, supervised, then unsupervised levels
- Full tracing and runbook at handover
Conversations That Finish
Chatbot and conversational AI
Customer support assistants, voice agents and AI receptionists, internal assistants, and multi-channel messaging across web, WhatsApp, SMS and voice. Grounded in your own content, connected to your real systems, with an explicit refusal boundary and clean human handover.
What is included
- Grounded retrieval over your documentation
- Tool access so the request gets completed
- Voice runtime at a 95ms speech-to-speech target
- Evaluation graded on your real transcripts
Autonomous Agent Loops
Loop Engineering
Systems that run AI agents unsupervised on recurring, machine-checkable work: the loop finds the work, hands it to an agent, verifies the result against your own historical data, and records what happened. Built with a maker/checker split and hard stops, so your team can walk away while it runs.
What is included
- Four-condition workload assessment
- Mechanical verification gates & replay
- Maker/checker agent splits
- Skill files, connectors & persistent state
Teams, products and infrastructure
The people and the platform behind the work. Senior engineers who embed in your team, full product builds from idea to launch, and the cloud foundation that keeps AI workloads reliable and affordable as they scale.
Embedded Engineering
Forward Deployed Engineers
Senior engineers who join your standups, work in your codebase, and ship production AI on your stack. Accountable for a working system rather than a timesheet, with a proof of concept in 2 to 4 weeks and production in 8 to 14, and knowledge transfer built in so your team runs the result.
What is included
- One scoping call, no discovery invoice
- You meet the engineer before committing
- PoC against your real data in 2 to 4 weeks
- Full source-code handover at completion
Embedded AI Engineering Capacity
Hire AI developers
AI and LLM engineers, machine learning engineers, agent engineers, and data and platform engineers placed as an embedded team inside your organisation, as an alternative to a six to nine month recruit. Remote-first from Surat and Bengaluru, with timezone coverage for the Americas, Europe and Asia.
What is included
- Single engineer or a small pod
- Embedded in your standups, Slack and repos
- Outcome-scoped engagements, not open retainers
- Documented handover at completion
AI Product Development
SaaS product development
Full AI-powered SaaS products from idea to launch. Our team handles product design, engineering, AI integration and deployment, so you go to market with a production-ready product without needing to build an in-house AI team.
What is included
- Product architecture and system design
- Frontend and backend engineering
- AI feature integration & optimisation
- Launch, scaling and post-deployment support
AWS Cloud Engineering
AWS and infrastructure scaling
Deep, end-to-end AWS expertise for AI and product workloads. We design and operate systems across SageMaker, EC2, Bedrock, Lambda, ECS, EKS and the data stack, with predictive cost analysis built in so spend scales with real usage. On a recent regulated workload, predictive GPU auto-scaling cut monthly compute by about 40%.
What is included
- SageMaker training, deployment & MLOps pipelines
- EC2 GPU & Spot optimisation with predictive autoscaling
- Serverless and container AI workloads
- Data engineering on AWS (S3, Glue, Athena, Redshift)
Industry solutions
Packaged offers for one industry at a time, where the data sources, the compliance design and the first release are already defined, so the engagement starts with an assessment rather than a blank page.
AWS, Databricks and Claude for pharma
Pharma quality and manufacturing intelligence
A governed intelligence layer across MES, LIMS, QMS, ERP, batch records and SOPs for pharmaceutical manufacturers in India and Canada. Databricks correlates and computes, Claude reasons over governed evidence, and the first release ships a deviation investigation copilot, CAPA intelligence and manufacturing quality intelligence. Designed to support a validated environment. AI never replaces the quality decision.
What is included
- Two to four week readiness and quality assessment first
- Deviation copilot, CAPA intelligence, quality dashboards
- Deterministic computation, ML and Claude each with one job
- Audit trail of evidence, model, prompt and approver on every draft
How an engagement runs,
and how long it takes.
Discover
Weeks 1 to 2
We learn your business, data, and goals before any code, with your security and compliance people in the room where the work touches regulated data. You receive an architecture, a costing, and an honest read on what should not be built.
Design
Inside discovery
Architecture, model selection, and data strategy designed for your use case and scale, taken through your review process while it is still cheap to change.
Build
Proof of concept in 2 to 4 weeks
Engineers and designers ship production-grade AI with the right cost, latency, and accuracy trade-offs, against your real data rather than a sanitised copy, with evaluation built alongside the system rather than added at the end.
Scale
Production in 8 to 14 weeks
Most systems reach production between eight and fourteen weeks from kickoff, larger platform programmes in twelve to sixteen. We stay beside you afterwards by choice, not because the system cannot run without us.