AI strategy & use-case discovery
Find where agents create real value and build a roadmap your leadership can fund.
- Workflow and data-readiness assessment
- Use cases ranked by value and feasibility
- Architecture, governance and ROI plan
End-to-end AI delivery, from strategy to operations
Engage us for a single stage or the whole lifecycle. Every engagement is led by senior engineers and measured against outcomes you define at the start.
Agentic AI & multi-agent systems
Agents that reason, plan and act across your applications with clear decision boundaries.
- LangGraph, LlamaIndex and MCP tool integration
- Supervisor and multi-agent orchestration
- Human-in-the-loop approvals and audit trails
RAG & enterprise knowledge assistants
Grounded assistants over documents, tickets, contracts and databases, with citations.
- Pinecone, FAISS and platform-native vector search
- Chunking, hybrid search and reranking
- Permission-aware retrieval
LLM evaluation & guardrails
Measure and protect quality before launch and after every change.
- Ragas, DeepEval and GENEVAL test suites
- Safety, PII and prompt-injection guardrails
- Regression evaluations in CI/CD
LLM fine-tuning & deployment
Adapt open and commercial models to your domain and serve them efficiently.
- Fine-tuning Hugging Face and open models
- Model routing for cost and latency
- Private deployment inside your cloud
Data engineering & LLMOps
The data pipelines and operations that keep AI reliable at scale.
- Lakehouse and warehouse pipelines
- Tracing, monitoring and cost controls
- Managed AI operations and support
Case studies
Agentic AI we have taken into production
Five builds across cloud operations, finance, intellectual property and banking. Each pairs agents with the client's data, measurable evaluation and a person who makes the final call.
Illustrative architecture with sample data · built on Clyros.AI.
Cloud & IT operations
Multi-agent cloud operations assistant
One conversation for cost, security, incidents and changes across AWS, Azure, Google Cloud and Databricks, with a person approving every change.
- Industry
- Cloud & IT operations
- Platforms
- AWS · Azure · Google Cloud · Databricks
- Pattern
- Supervisor + specialist agents
- Control
- Human approval on every change
Read the full case study
The challenge
Cloud teams were switching between four consoles, billing exports and runbooks to answer simple questions such as why spend jumped or which resources were exposed. Findings lived in different tools, and fixes depended on whoever knew each platform best.
What we built
- A supervisor agent that plans each investigation and routes it to specialist agents for cost, security posture, incidents and changes.
- Tool access through MCP servers over each cloud's APIs, read-only by default.
- A change agent that drafts infrastructure-as-code fixes but cannot apply them on its own.
- A full audit trail of every tool call and decision.
Safety & governance
- Least-privilege, read-only credentials for investigation
- Write actions only after a named approver signs off
- Every agent step logged and replayable
- Evaluation suite for tool-call accuracy before each release
What the team can do now
- Engineers ask questions in plain English instead of switching consoles
- Cost, security and incident findings arrive in one place with their evidence
- Fixes are drafted in minutes and still go through review
- The same assistant works across all four platforms
LangGraphMCP serversClaudeAWSAzureGoogle CloudDatabricksTerraformGENEVAL
Illustrative architecture with sample data.
Financial services
Finance knowledge assistant with RAG
Analysts ask questions across annual reports, filings, policies and management accounts, and get answers that cite the exact page and table.
- Industry
- Financial services
- Pattern
- Retrieval-augmented generation
- Evaluation
- Ragas golden question set
- Control
- No answer without a source
Read the full case study
The challenge
Finance and research teams spent hours finding figures and commentary across long reports, filings and spreadsheets. General chatbots could not be trusted with numbers, and answers needed to show exactly where they came from.
What we built
- Table-aware ingestion that keeps rows, columns, units and periods intact, with chunks that follow document sections.
- Hybrid retrieval (dense + keyword) with re-ranking and filters for entity, period and document type.
- Answers that quote figures from the sources and cite page and table for every claim.
- Permission-aware search, so users only see documents they are allowed to read.
Safety & governance
- Refuses to answer when no supporting source is found
- Numbers are quoted from documents, not calculated by the model
- PII masking before indexing
- Ragas evaluation gate before every release
What the team can do now
- Analysts get cited answers instead of searching document by document
- Every figure can be checked against its source in one click
- Quality is measured on a fixed question set, release after release
- New documents are searchable as soon as they are ingested
LangChainLlamaIndexPineconeFAISSHybrid searchRagasDeepEval
Illustrative architecture with sample data · built on IPAI.
Intellectual property
IP search and patent drafting assistant
Semantic prior-art search across patents and journals, plus a drafting assistant that proposes claims and differentiation notes for attorneys to refine.
- Industry
- Intellectual property & R&D
- Sources
- Patents + journal literature
- Pattern
- Semantic search + drafting agent
- Control
- Attorney review on every draft
Read the full case study
The challenge
Prior-art searches relied on keyword queries that missed documents describing the same idea in different words, and first drafts of claims took a large share of attorney time.
What we built
- Semantic search that matches on meaning across patents and journal articles, with suggested classification codes and family de-duplication.
- Ranked results with similarity scores and the passages that overlap with the invention.
- A drafting assistant that proposes claim language and differentiation notes against the closest documents.
- A prior-art report that links every statement back to its source passage.
Safety & governance
- Drafts are suggestions; a registered attorney edits and signs off
- Every overlap claim links to its source passage
- Confidential disclosures stay inside the client's environment
- Search and drafting runs are logged
What the team can do now
- Searches surface relevant documents that keyword queries miss
- Attorneys start from a structured first draft instead of a blank page
- Prior-art reports are consistent and traceable to sources
- Patent and journal literature are searched together
Semantic searchVector databaseLLM drafting agentCitation linkingIPAI
Illustrative architecture with sample data · built on IPAI.
Intellectual property
GenAI patent infringement analysis
Claims split into limitations and mapped element by element to product evidence, producing a draft claim chart that counsel confirms or rejects.
- Industry
- Intellectual property & litigation support
- Output
- Draft claim charts
- Pattern
- Claim parsing + evidence mapping
- Control
- Counsel confirms every mapping
Read the full case study
The challenge
Building claim charts meant reading datasheets, teardown reports and images against each claim limitation by hand. The work was slow, and it was hard to see quickly where evidence was strong, partial or missing.
What we built
- Claim parsing that splits each claim into limitations and uses the patent's own definitions for claim terms.
- Evidence mapping across datasheets, teardown reports, images, product pages and standards documents.
- A draft claim chart that rates each limitation as strong, partial or no evidence, with the exact supporting passage.
- An explicit 'no evidence found' outcome instead of forced matches.
Safety & governance
- Every mapping links to the exact evidence passage
- 'No evidence found' is always an allowed answer
- Attorneys confirm, edit or reject each mapping
- Runs are logged for privilege review
What the team can do now
- Counsel starts from a structured draft chart rather than raw documents
- Gaps in evidence are visible immediately
- Every mapping is traceable to a source
- The same method applies across patents in a portfolio
LLM claim parserMultimodal evidence extractionRetrievalClaim chart generatorAudit logging
Illustrative architecture with sample data.
Banking
Bank KYC on a semantic layer with agentic workflows
KYC documents and banking data unified in a governed semantic layer, with agents that check completeness, screening and risk, and a compliance officer who decides.
- Industry
- Banking & compliance
- Platforms
- Databricks · Snowflake
- Pattern
- Semantic layer + agentic flow
- Control
- Compliance officer sign-off
Read the full case study
The challenge
KYC data was spread across scanned documents, account-opening forms, core banking and screening tools, and each team defined terms like 'complete KYC' or 'high risk' slightly differently. Reviews were manual and hard to audit.
What we built
- A semantic layer with one governed definition of customers, beneficial owners, accounts, KYC completeness, risk rating and re-KYC dates.
- An extraction agent that reads KYC documents, fills fields and flags mismatches against core banking.
- Agents for completeness, sanctions and PEP screening, and risk scoring, all using the semantic layer's rules.
- Plain-English questions answered through the semantic layer, so every answer uses the same definitions.
Safety & governance
- Agents prepare cases; a compliance officer approves or rejects
- Screening results include the reason for each hit or clear
- Row-level access controls from the data platform
- Complete audit trail for regulators
What the team can do now
- One definition of KYC status shared by agents, reports and teams
- Reviewers receive a prepared case file instead of raw documents
- Missing documents are identified and chased automatically
- Compliance questions are answered in plain English with consistent logic
Semantic layerDatabricks Unity CatalogSnowflakeDocument AIAgent workflowsHuman-in-the-loop
Delivery lifecycle
What every engagement includes
Discover
Workshops with process owners, data review and a ranked use-case list.
Use-case briefBuild
Working software every sprint, in your repositories and cloud accounts.
Sprint demosEvaluate
Golden datasets, automated metrics and red-team tests signed off by you.
Quality gatesDeploy
Infrastructure as code, access controls, monitoring and rollback plans.
Production releaseOperate
Support, continuous improvement and handover with runbooks and training.
Ongoing
Engagement options
Pick the shape that fits the work
Start small with a fixed-scope pilot, or bring in a full team from day one.
Fixed-scope pilot
A defined use case taken to a production-ready pilot with an evaluation report and go-live plan.
Dedicated AI pod
An FDE-led team of engineers working on your roadmap, billed monthly, scaled up or down each month.
Managed AI operations
We run, monitor and improve live agents for you with agreed service levels.
Not sure where to start?
A free discovery call helps you choose the right use case, platform and team shape.