AI agents that do real work, inside your systems.
YashodhanTek designs, builds and runs production AI agents for enterprises. They connect to the tools your teams already use, follow your policies, and are tested against your own data before they go live.
- PlanSplit into 3 sub-tasks: open blockers, linked merges, owner load
- Query tools over MCPjira.search fixVersion = 4.2 AND priority >= High JiraConfluenceGit
- Correlate11 high-priority issues, 4 with no merged fix and less than 3 days left
- Policy check passedRead-only scope; no write actions requested
Illustrative scenarios showing the trace, cost and guardrail data every production run records.
Most AI pilots never reach production. Ours are built for it from day one.
A demo answers ten friendly questions. Production means thousands of real ones, every day, with an audit trail. The difference is engineering.
A typical pilot
- Tested on a handful of examplesAccuracy unknown on real traffic.
- Different answer every runNo way to tell if a model update broke it.
- Costs discovered on the invoiceSilent retries and long prompts add up.
- No record of what it didSecurity and audit teams say no.
An agent built by YashodhanTek
- Measured against your golden datasetAccuracy targets agreed before the first prompt.
- Regression tests on every changeAnswers, tool calls and paths are all checked.
- A token budget per workflowCost per task on a dashboard from week one.
- Every action logged and policy-checkedApprovals where you want a person in the loop.
What we do
Six services that cover the full path, from choosing the right workflow to running the agent in production.
Custom and multi-agent systems
A planning agent breaks the request down, specialist agents do the work through your tools, and a reviewer checks the result before anything is sent.
Multi-agent engineeringStrategy and readiness
Find the three workflows where agents pay back fastest, with a 90-day roadmap.
StrategyKnowledge assistants
Answers from your documents, with citations and role-based access.
Knowledge assistantsAgentic automation
Automation that reads documents, decides the next step and asks a person when unsure.
Agentic automationAgentOps
Evaluations, guardrails, tracing and cost control for agents in production.
AgentOpsAI-ready platforms
Kubernetes, cloud, DevOps and disaster recovery for AI workloads.
Platform engineeringFrom first workshop to production in about twelve weeks.
Each phase ends with something you can test, so you decide whether to continue with evidence in hand.
Discover
Weeks 1–2Map workflows, data, tools and risk. Choose the first use case and agree the golden dataset.
Prototype
Weeks 3–6A working agent on your real data, scored against the dataset every day.
Harden
Weeks 7–11Guardrails, policy checks, approval steps, tracing and token budgets.
Launch
Week 12Phased rollout with dashboards and a tested rollback plan.
Operate
OngoingMonthly accuracy and cost reviews. New tools and skills added as you grow.
Where agents pay back first
Work that is document-heavy, spread across several tools and governed by clear rules.
Semiconductors and high-tech
Engineering teams with release trains, test farms and deep Jira histories.
- Release managementStatus, blockers and release notes on request.
- Engineering knowledge searchAnswers from specs, wikis and past tickets.
- Test-log triageGroup failures and route them to the right owner.
Distribution and logistics
High-volume operations where exceptions consume the most staff time.
- Order exceptionsResolve mismatches before they reach a customer.
- Supplier onboardingCollect, check and file supplier documents.
- DR readinessContinuous checks against recovery runbooks.
Banking and insurance
Document-heavy, regulated work where every decision needs evidence.
- KYC reviewExtract, verify and flag gaps in customer documents.
- Policy Q&ACited answers for operations and branch staff.
- Compliance evidenceGather audit evidence across systems on schedule.
Media and OTT
Large catalogues and constant publishing across apps and devices.
- Metadata taggingGenres, cast and moods tagged at ingest.
- Subtitles and promosGenerated drafts for editors to approve.
- Viewer supportAccount and playback help in every app.
Healthcare and life sciences
Clinical and operational content with strict privacy controls.
- Document summariesSummaries of long clinical and trial documents.
- Patient engagementAppointment and care-plan assistants.
- SOP assistantAnswers from validated operating procedures.
Education
Universities and training providers serving thousands of learners.
- Course contentDrafts of lessons, cases and assessments.
- Student supportAdmissions, fees and timetable questions.
- Faculty assistantGrading support with rubric citations.
A release-management agent team for a global semiconductor engineering organisation.
Programme managers used to assemble release status by hand from thousands of Jira issues. Now they ask in plain English and get a cited answer, with any write action checked against policy first.
- Agents
- Planner, executor and display agents with shared procedural memory
- Integrations
- Jira and internal tools through MCP servers
- Safeguards
- Policy-as-code checks before every action
- Quality
- Golden-dataset evaluation of answers, tool calls and paths
- Stack
- Google ADK, LiteLLM, Redis, Azure OpenAI and open-weight models
Governance your security team will sign off
Agents act on real systems, so control is designed in rather than added later.
Runs in your cloud
Agents, vector stores and model gateways deploy inside your Azure, GCP or AWS tenancy.
Policy as code
Rules written in OPA are checked before every tool call, not just at login.
A complete audit trail
Every prompt, tool call and decision is traced and kept for review.
People stay in charge
You choose which actions need human approval, and agents wait for it.
Ways to work with us
Start small, see results on your own data, then scale what works.
Agent discovery sprint
Fixed price, 2 weeks
- Workflow and data assessment
- Ranked use cases with business case
- Reference architecture
- Working demo on your data
Build pod
Dedicated team, 8–12 weeks
- Architect, engineers and QA
- One agent taken to production
- Evaluation harness and guardrails
- Handover with full documentation
Managed AgentOps
Monthly retainer
- Monitoring and incident response
- Regression tests on model updates
- Monthly accuracy and cost report
- New tools and skills each quarter
Questions we hear most
Something else on your mind? Ask us directly.
How long does it take to get an agent into production?
Most first agents reach production in 8 to 12 weeks: a 2-week discovery sprint, 2 to 4 weeks to a working prototype on your data, and 4 to 6 weeks of hardening with evaluations, guardrails and approvals.
Which models and frameworks do you use?
We are model-agnostic. We work with Azure OpenAI, Google Gemini, Anthropic Claude and open-weight models such as Qwen, and frameworks including Google ADK, LangGraph and Microsoft Agent Framework. We pick per task based on accuracy, latency and cost.
Does our data leave our cloud?
It does not have to. We deploy agents, vector stores and model gateways inside your Azure, GCP or AWS tenancy, use private model endpoints where available, and log every tool call for audit.
How do you prove an agent is accurate?
Before we build, we agree a golden dataset of real questions and expected outcomes with you. Every release is tested against it, covering the final answer, the tools the agent called and the path it took.
Can agents take actions, or only answer questions?
Both. Agents can create tickets, update records or trigger workflows through your APIs and MCP servers. Write actions run through policy checks and, where you choose, a human approval step.
What does it cost to run an agent?
Running cost depends mainly on model choice and volume. We set a token budget per workflow, show cost per task on a dashboard and route simpler steps to cheaper models.
Bring us the workflow that eats your team's week.
In a 30-minute call with a senior architect, you'll learn whether an agent can take it on, what it would cost to build and run, and how we'd prove it works.