Automation that survives contact with your actual workflow
Most AI projects stall because nobody mapped the work before buying the licence. We do the mapping, the build, the governance and the training that decides whether any of it gets used.
Two weeks watching the work before anyone picks a model
We map how the task is done today, where the data sits and who owns it. You get three candidates ranked by payback and a build-or-buy call in writing — before a licence is bought.
- Task and volume audit across the team
- Where the data lives and who owns it
- Three candidates ranked by payback
- A build-or-buy recommendation, in writing
Reading the paperwork nobody wants to read
Invoices, purchase orders, GRNs, KYC packets, claims and tender documents — extracted into the system that already holds them, with an exception queue for anything the model is unsure about.
- Invoice and PO extraction into your ERP
- KYC and onboarding document checks
- Contract clause extraction and expiry tracking
- Scanned, handwritten and vernacular documents
Deflecting the tickets that repeat every week
Around half of a typical internal helpdesk is the same twenty requests. Those get answered from your own runbooks; anything outside them is routed to a person with the context already attached.
- Password, access and VPN self-service
- Triage and routing on intent, not keywords
- Draft responses written from your runbooks
- Escalation the moment confidence drops
Answers from your own documents, with the source attached
Retrieval across SharePoint, Google Drive, Confluence and file shares — permission-aware, so a user only ever sees what their directory group already allows.
- Retrieval across Drive, SharePoint and file shares
- Permissions inherited from your directory
- A citation on every answer
- Scheduled re-indexing, not a one-off load
The spreadsheet steps between two systems
The work that exists only because two systems do not talk. Reconciliation, matching and report assembly run unattended, and only the exceptions reach a person.
- Bank statement and settlement reconciliation
- Vendor master cleaning and duplicate detection
- Report assembly and scheduled distribution
- Exception queues for humans, not blanket approval
Giving the field team their evening back
Visit notes, quotations and coverage reports written from what the engineer already said into their phone — in the language they actually speak.
- Visit notes turned into CRM entries
- Quotation and BOM drafting from a spec
- Vernacular voice notes transcribed and filed
- Daily route and coverage summaries
When one answer is not enough
Multi-step agents that call your systems rather than just describe them. Every tool is named and scoped, every write waits for a human, and every action is logged.
- Tool access scoped to named systems only
- Human approval on anything that writes
- A full audit trail of every action taken
- A kill switch and a rollback path by design
Cloud, private or on-premise — the data decides
Regulated data does not always get to leave the building. We deploy against hosted models where that is fine and run inference inside your own estate where it is not.
- Azure OpenAI and AWS Bedrock deployments
- Private VPC and on-premise inference
- India data residency where the rule requires it
- No training on your data, in the contract
DPDP, access control and what the model may see
The questions your auditor will ask, answered at design time rather than after the first incident.
- Role-based access inherited from your directory
- PII masked before a prompt leaves the estate
- Prompt and output logging retained for audit
- Model and version pinning under change control
Pilot against a number, then scale
A four to six week pilot with a baseline measured before it starts, so the decision to scale is arithmetic rather than opinion. Training for the team that inherits it, and a support line afterwards.
- Baseline measured before, not estimated after
- Four to six week pilot on one workflow
- Training for the team that will own it
- Handover pack and a named support contact
Platforms we build AI workloads on
Cloud and productivity platforms
Tell us what you are running today
We will tell you what we would change, and what it would cost. No obligation, and a named engineer on the call rather than an account manager.