You're blind between reports
Whether it's a line, a ledger or a queue, problems surface hours late, long after the cost is locked in.
AI Agents · Agentic Intelligence
Plans do not fail.
Plans do not fail.
Execution does
An invoice sits unapproved, a shipment slips, a ticket ages, and the week falls behind. We build agents that catch it and act.
The problem
Where the week quietly slips away
Rarely from one big failure. Usually from a dozen small ones nobody had time to catch, in the plant, the back office, the service desk or the supply chain.
Your knowledge walks out
Why the press drifts on Mondays, why that customer always disputes. It lives in one person's head, not a system.
You're stuck firefighting
Your team chases the loudest problem, not the one quietly costing the most.
The answer
Put an agent on watch that never looks away
Same idea whichever part of the business you point it at.
Chat with your data
Ask in plain language; get the answer and the reason in one line, with no dashboards to dig through.
Get answers written up
Summaries, breakdowns and reports generated on demand for a shift, a ledger, a region or a queue.
Automate the follow-through
At-risk work flagged and the right person alerted automatically, while there's still time to fix it.
How we work
We don't sell you an agent.
We don't sell you an agent.
You bring the problem. We build it
No catalogue to pick from. The engineering is the same wherever it points: perception, reasoning, tool use, guardrails. Describe the bottleneck and we build the agent around it.
01 · You
Your problem statement
In your words, not ours. No template, no readiness questionnaire.
“Six people spend every morning
reconciling three systems by hand.”
02 · Us
We scope it
We work out whether an agent is the right answer here, and say so if it isn't.
- Where the data lives
- Which systems it must touch
- What it must never do alone
- What “good” looks like, in numbers
03 · The agent
Built for your process
Four things, on a loop, for as long as the work runs.
- Perceive. Reads your data
- Reason. Plans the steps
- Act. Uses your systems
- Report. Shows its working
04 · Outcome
Work that runs itself
Judged against the numbers you named in step 01, not a demo script.
Measured, then tuned
You approve what matters.
Every action is logged.
Tuned against real results, not a demo
How we'd build it with you
From problem statement
From problem statement
to something running in production
A typical build runs 12 to 20 weeks depending on how many systems it has to touch. You see working software long before the end of it.
Discover
We sit with the people doing the work and map what actually happens, not what the process document says.
You get: shortlist of candidate use cases with an honest value estimate
Identify
We pick the one worth doing first: enough value to matter, enough data to be feasible.
You get: scoped spec, data readiness check, success metrics agreed up front
Design
Architecture, agent roles, guardrails and the integration surface. Where a human must stay in the loop is decided here, not later.
You get: technical design and a working proof on your own data
Develop
We build the agents, the connectors and the models, and put them in front of your team early enough that feedback still changes things.
You get: a working agent you can try, iterated weekly
Integrate
Secure connections into your environment, access control, and testing against real cases, including the ugly ones.
You get: tested system, security review, go-live plan
Improve
Agents get better with use. We tune against what really happened and extend to the next process once this one has earned it.
You get: performance reporting and a roadmap for what's next
Engineering capability
The building blocks we put to work
Perception, reasoning, tool use and guardrails, applied through disciplines like these. Whichever combination your process calls for, it's assembled from the same underlying engineering.
Agentic AI & multi-agent systems
Agents that reason through multi-step work, call your tools and APIs, and hand off to each other under an orchestrator that holds the plan.
Reason · Tool-use · OrchestrateWorkflow & process automation
End-to-end pipelines that stitch AI into the systems you already run, from ERP and CRM to MES, ticketing and email, so a whole process completes without a hand-off queue.
Connectors · Orchestration · RetriesMachine learning & prediction
Forecasting, anomaly detection, classification and remaining-life models, wired in as the agent's judgement rather than a report nobody opens.
Forecast · Detect · RankConversational & voice agents
Chat and voice front-ends that don't just answer. They look things up in your systems and complete the request on the caller's behalf.
Understand · Retrieve · ResolveKnowledge based
Your manuals, SOPs, contracts, tickets and history made queryable, so the agent answers from what your organisation already knows, with the source attached.
Index · Retrieve · CiteDocument understanding
Drawings, invoices, certificates, inspection reports and the forms nobody has digitised yet. Read them, pull out the fields that matter and put them somewhere structured, with the original still attached.
Extract · Validate · FilePlanning & optimisation
Schedules, routes, allocations and sequences worked out against your real constraints rather than a spreadsheet habit. When something changes at 10am, the plan is redone instead of patched.
Constrain · Solve · ReplanEvaluation & guardrails
An agent is only useful if you can tell when it is wrong. Test sets, scored runs, thresholds it must clear before it goes live, and a defined way for it to stop and ask rather than guess.
Test · Score · Gate
Shapes of the work
Problems, and the agents
Problems, and the agents
we'd build to order
These aren't products on a shelf. They're patterns. Each one starts with a problem statement a real team has said out loud. If yours sounds like one of these, we already know where to start. If it doesn't, tell us anyway.
Document & invoice processing
“Our team keys data out of PDFs and emails all day, and we still find mistakes downstream.”
We'd build: an agent that ingests the documents, extracts the fields, checks them against your rules and master data, posts the clean ones straight into your system and escalates only the genuine exceptions.
OCRLLM extractionValidation rulesERP API
You'd measureCycle time per document, straight-through rate, exception rate, downstream corrections.
Continuous compliance checking
“We only find out we've drifted out of policy when the auditor tells us.”
We'd build: an agent that reads the rules and your operational records side by side, tests them continuously instead of quarterly, flags what breaks and drafts the evidence pack before anyone asks for it.
Policy ingestionRule engineAudit trailAlerting
You'd measureFindings caught before audit, time to close an issue, hours spent on evidence collection.
Supplier and purchase-order follow-up
“Half our week goes on chasing suppliers for dates nobody updates.”
We'd build: an agent that watches every open purchase order, asks the supplier for a confirmed date on your schedule, reads the reply, updates the ERP and escalates only the lines that are genuinely at risk of stopping a build.
Email & portal readingERP write-backRisk scoringEscalation rules
You'd measureOn-time supplier confirmations, lines at risk caught early, hours spent chasing, stockouts avoided.
Scheduling & resource allocation
“One person holds the whole schedule in a spreadsheet, and one late order breaks the rest of the week.”
We'd build: an agent that holds the constraints, re-plans when reality moves, explains the trade-off it made in plain language and pushes the change back into your planning system.
Constraint solverERP / MESNatural-language briefApproval gate
You'd measureOn-time delivery, schedule adherence, replan turnaround, utilisation.
Customer-facing service agent
“Most of what our team answers is the same six questions, and we still can't cover nights.”
We'd build: a chat or voice agent grounded in your product and policy data that resolves the routine end to end, whether that's order status, returns, bookings or account changes, and hands over with full context when it shouldn't decide alone.
Voice / chatRAG on your contentTransactional APIsHuman handover
You'd measureContainment rate, handling time, CSAT, out-of-hours coverage.
Analysis & reporting
“Three days a month disappear into building a report that's stale the day it lands.”
We'd build: an agent that pulls from every source itself, runs the analysis, writes the narrative of what changed, why, and what to do, then publishes on a schedule or on demand, in the format your board already reads.
Data connectorsAnalysis modelsNarrative generationBI / export
You'd measureHours to produce, freshness at read time, decisions made from it, requests for re-cuts.
Internal knowledge assistant
“The answer exists somewhere, in a PDF, a thread, or one person's head, and finding it takes half a day.”
We'd build: an agent over your SOPs, drawings, tickets and wikis that answers with the source cited, respects who's allowed to see what, and tells you when it doesn't know instead of inventing.
Vector searchPermission-awareCitationsFeedback loop
You'd measureTime to answer, answer acceptance rate, repeat questions, new-starter ramp time.
Where agents earn their keep
Different industries.
Different industries.
The same kind of bottleneck
Every one of these is a place where skilled people spend their day gathering, checking, chasing and re-keying. That is the work an agent is good at.
Manufacturing
Downtime cause, quality escapes, schedule risk, maintenance planning, shift handover.
Logistics & supply chain
Late-order prediction, freight booking, exception handling, supplier chasing, landed-cost checks.
Finance & accounting
Invoice capture, duplicate and fraud detection, reconciliation, collections follow-up, close reporting.
Banking & insurance
KYC and onboarding checks, claims triage, underwriting support, AML alert review, dispute handling.
Customer service
Tier-one resolution, order and returns handling, out-of-hours cover, context-rich handover to people.
IT & internal ops
Ticket triage, access requests, incident summarising, change-impact checks, onboarding workflows.
Healthcare & pharma
Scheduling, records summarising, prior-authorisation packs, batch documentation, deviation review.
Construction & projects
RFI and submittal handling, progress reporting, drawing revision checks, subcontractor chasing.
Retail & e-commerce
Stock-out prediction, listing and content generation, pricing checks, order exception resolution.
Energy & utilities
Asset condition monitoring, outage triage, field-crew dispatch, regulatory reporting.
Legal & compliance
Contract review, obligation tracking, policy testing, evidence collection, regulatory change watch.
HR & people ops
Screening and scheduling, onboarding paperwork, policy questions, case triage, compliance training.
Not on the list? Tell us anyway. These are only examples. If the work is repetitive, judgement-heavy and spread across systems, it's worth a conversation.
Under the hood
The same architecture,
The same architecture,
pointed at your problem
Follow one request through it. What changes between projects is the agents and the connectors, never the foundations underneath them.
Something arrives
A question typed in chat, an email landing, a sensor reading crossing a limit, a check that runs every morning. However the work shows up, it comes in through the same door.
The orchestrator plans
It works out what the request actually needs, in what order, and which agent should handle each step. It holds the state, so nothing is dropped halfway.
Agents do the work
Specialists take their turn. One gathers the facts and cites them, one reasons about what they mean, one carries out the action it decided on.
Your systems update
The result is written back to whichever system owns it, the person who needs to know is told, and every step is kept so you can see how it got there.
Underneath all four, on every project
Your knowledge, indexed and cited
Connectors into ERP, CRM, MES and APIs
Roles, guardrails and a full audit trail
Runs on your cloud, on-premise or at the edge
Same agent, different job
Ask in plain language.
Ask in plain language.
Get the "why" instantly
Pick a domain. The pattern is identical. The agent reads live data, explains the cause, and offers to act.
Why is our payables run higher than last month?
Payables are up 14%. Most of it is one vendor: 62 invoices from Kestrel Supply posted twice under different PO references. I've held the duplicates pending review. That's ₹18.4L not yet paid out. Want the list?
Yes. And stop this happening again.
Sent. I've added a duplicate check on vendor + amount + date across PO references, and I'll route anything it catches to you before posting.
- Reads: your ERP ledger, invoices, PO and vendor master
- Explains: what moved the number and who caused it
- Acts: holds payment, writes a new control, escalates
- Nothing pays out without the approval you configured
Which customer orders are going to be late this week?
Four. Three trace to one cause: the Chennai consolidation hub is running 26 hours behind, so shipments SO-4471, SO-4489 and SO-4502 miss their promise dates. The fourth is a short-pick on a component with no alternate supplier.
What can we do about the three?
Re-routing them direct clears all three with a ₹41k freight premium. I've drafted the carrier booking and the customer notice. Both are waiting on your approval.
- Reads: orders, inventory, carrier and tracking feeds
- Explains: the shared root cause, not four separate alarms
- Acts: prices the options, drafts the booking and the email
- Shows the trade-off so the call stays yours
What's driving our ticket backlog?
Backlog is 412, up 60 this week. 38% of new tickets are the same issue, users locked out after the SSO policy change on Tuesday. I can resolve those from the runbook without an agent touching them.
Do it, and tell me what's left.
157 auto-resolved and confirmed with each user. What's left is 255 genuine tickets, and the top theme is VPN latency from the Pune office. I've opened a problem record and tagged the network team.
- Reads: your ticketing system, runbooks, change log
- Explains: the pattern behind the volume, not ticket-by-ticket
- Acts: resolves the routine, escalates the real problem
- Works the same for HR, facilities or customer support
What's our torque spec for the M12 flange bolts on the export variant?
95 Nm ±5, per WI-2231 rev C (issued March, supersedes rev B which said 85 Nm). The export variant also needs the thread locker in step 7. That clause only exists in the export annex, so it's easy to miss.
Who signed off rev C?
Quality Engineering, approved 14 March. Here's the document and the change note. If you need the rationale, it's in the linked deviation report.
- Reads: SOPs, drawings, contracts, tickets, wikis, email
- Cites the source and the revision, every time
- Respects permissions. People see only what they should
- Says "I don't know" instead of inventing an answer
Why is OEE down on Line 2 today?
Line 2 OEE is 71% vs 84% yesterday. The drop is availability: Press-03 had 3 unplanned stops (48 min total), all tagged tool change overrun. It's the top loss driver this week. Want me to flag it to maintenance?
Yes, and alert me if it repeats.
Done. Maintenance notified, and I'll escalate to you if Press-03 tool changes exceed 12 min again this shift.
- Reads: machine, downtime, production and quality data
- Explains: which loss driver, which asset, which moment
- Acts: notifies maintenance, sets a standing watch
- This one is live today as TARA
These conversations are examples of how you would talk to an agent, not transcripts. TARA, the operations agent, is live today. The rest are built to order, scoped around the systems you already run.
AI navigation
No typing.
No typing.
Just say it
Say where you want to go and the platform takes you there, hands free, gloves on.
“Show me this week’s payables by vendor”
“Which supplier deliveries are running late”
“Show me the tickets still unanswered”
“Find the changeover procedure for Line 2”
“Show me health parameters for Press-03”
Works on the shop-floor panel, a phone or the control room screen. Useful when your hands are busy and a keyboard is not.
The differenceNobody should have to hunt
Nobody should have to hunt
for an answer that already exists
Without the agent
Answers cost hours
- Someone exports data and builds the report by hand
- The "why" is debated, rarely proven
- Problems are noticed after the cost is already locked in
- Insight lives with whoever knows the dashboards
With an agent on it
Answers in a sentence
- Ask in plain language, get the answer instantly
- Root-cause explained, traced to the record and the moment
- At-risk work flagged before it slips
- Everyone gets the same answer, any time
Before you bring us a problem
FAQs
From your own live APMS data: machine status, downtime reasons, production and quality. It reasons over that data, so answers reflect what's actually happening on your floor, not generic advice.
Yes, within limits you set. It can raise alerts, escalate by role and schedule follow-ups automatically, and you decide which actions need a human to confirm first.
No. Your production data stays yours and is used to answer your questions, not to train shared or third-party models.
A working proof on your own data during the design phase, typically weeks rather than quarters. A full production build usually lands in 12 to 20 weeks depending on how many systems it has to integrate with and how clean the data is when we arrive.
Tell us the part of the week everyone dreads
See TARA watch a real floor, then tell us the process in your business that should run itself. We'll say honestly whether an agent is the right answer, and what it would take to build.