Operations lead and their teams
The people who run the busiest step need core ai to work on a normal Monday, not only in a demo with clean data.
Talk about your teamAI & automation
Core AI
Core AI built for Indian businesses, with one accountable team.
Scope, build and run core ai with measurable outcomes - no vendor handoffs, no black boxes.
- 01
Discovery
Map the current workflow and agree the smallest useful release. In practice this is the step the operations lead relies on when review notes must be right the first time.
- 02
Build
Ship in two-week increments with a staging link you can click. In practice this is the step the operations lead relies on when prompts must be right the first time.
- 03
Integrate
Payments, WhatsApp, email and accounting connected through documented APIs. In practice this is the step the operations lead relies on when retrieved context must be right the first time.
- 04
Hand over
Code, accounts and runbooks transferred to your ownership. In practice this is the step the operations lead relies on when decisions must be right the first time.
The bigger picture
Core AI, in practice.
Core AI sits inside our AI & automation practice. Scope, build and run core ai with measurable outcomes - no vendor handoffs, no black boxes. The useful question is not which platform to buy, but which decision or handoff the work should improve first.
Teams usually arrive with an LLM API, a vector store and the workflow engine already in place. The gap is rarely a missing tool; it is the undocumented step where prompts get copied between systems and someone compensates by hand.
We scope one measurable outcome, build it end to end, then extend based on evidence. Success is reviewed against task accuracy and human review time, with a working release every two weeks and a handover that leaves you in control.
The sections below describe how the engagement usually runs. Scope, integrations and timing are adjusted to your environment rather than copied from a standard package.
The challenges we address
The pilot never leaves the demo
It works on curated examples but has no evaluation set, no fallback and no owner for bad answers.
Data boundaries are unclear
Nobody can say which data may leave the system, which model sees it, or how it is retained.
Human review is undefined
Agents act on low-confidence cases because the escalation rule was never written down.
Prompts are treated as code
Changes are made directly in production with no versioning, tests or audit trail.
Cost is discovered late
Token spend is reviewed after the invoice, not per workflow or per customer.
Reporting depends on individuals
Preparing a review means exporting from the CRM and reshaping it by hand, so the numbers are always a little behind the decision.
Ownership is unclear
When a step fails, operations and the next team both assume the other one owns the fix.
Changes are not traceable
There is no dependable record of who changed prompts or why, which is exactly what an auditor asks for first.
Who this is for
Built around
the people who use it.
Multi-location ai & automation operators
Teams that need the same process, permissions and reporting across sites without a manual consolidation exercise at month-end.
Teams outgrowing spreadsheets
You have added people and volume, and the spreadsheet that once worked is now a risk that only one person fully understands.
Operations leaders standardising process
Leaders who need AI-assisted operations to behave consistently when a new hire, a new branch or a new client joins.
In-house IT and engineering teams
Teams that want a partner to ship the first release, document the decisions and leave a codebase they can extend.
Compliance and audit owners
People who need evidence on demand: who accessed what, which approval was given, and how long records are retained.
Founders shipping a first release
Small teams that need working software in weeks, with a scope they can defend to a client or an investor.
Teams migrating from a legacy tool
Organisations replacing the workflow engine without pausing the business or losing historical prompts.
Inside the scope
Useful intelligence. Built in.
Explore what goes into core ai.
Discovery
Map the current workflow and agree the smallest useful release. In practice this is the step the operations lead relies on when review notes must be right the first time.
Build
Ship in two-week increments with a staging link you can click. In practice this is the step the operations lead relies on when prompts must be right the first time.
Integrate
Payments, WhatsApp, email and accounting connected through documented APIs. In practice this is the step the operations lead relies on when retrieved context must be right the first time.
Hand over
Code, accounts and runbooks transferred to your ownership. In practice this is the step the operations lead relies on when decisions must be right the first time.
Reporting and MIS
Daily and weekly views built on the same records the team already maintains, so operations, data and support teams stop rebuilding spreadsheets before every review. The measures are chosen with you - typically task accuracy and human review time - and reviewed against a baseline agreed at kickoff.
Roles, permissions and audit trail
Access is granted by role and location, with a record of who viewed or changed prompts. Sensitive approvals are separated from routine edits, and break-glass access is logged so an urgent exception does not become an invisible one.
Data migration and reconciliation
Existing prompts, retrieved context and balances are inventoried, mapped and imported in a dry run before switchover. You get a reconciliation report and a read-only window on the old system, so a question about an old record does not stall the rollout.
Integrations and notifications
Documented connections to a vector store and the workflow engine, plus in-app chat for reminders and updates. Each integration has an owner, a failure mode and a retry path, instead of a webhook that silently stops delivering.
How we work
A clear path from start to finish.
BuildMade for your needs
ReviewReady for real use
LaunchSet up & handed over
- 01Week 1-2
Discovery and baseline
We map the current AI-assisted operations, interview operations, data and support teams and record where prompts is retyped. The output is a short written scope with one primary success measure and an explicit out-of-scope list.
- 02Week 2-3
Solution design
We choose the architecture and integration approach, including what to connect first, what to leave manual and how prompts will be governed. Trade-offs are documented rather than hidden in a diagram.
- 03Week 3-5
Build the first release
A working release ships every two weeks with a staging link. Reviews focus on real usage - exceptions, permissions, edge cases and the reports the team actually reads - not a feature checklist.
- 04Week 5-6
Integrate and validate
Connections to a vector store and the workflow engine are tested with representative data. We run an acceptance pass against the agreed metrics and fix the issues that block adoption before widening the rollout.
- 05Week 6-7
Pilot and training
One team uses the release for real work while others continue as before. Training is role-based, and the runbook is written for the team that inherits the system.
- 06Ongoing
Handover and iteration
Code, environments, documentation and credentials are transferred. We review task accuracy and human review time after launch, then agree the next increment only if the evidence supports it.
Why Synnotech
A practical approach. A clear outcome.
Your workflow, not a template
We start from how operations, data and support teams work today, then build core ai around the handoffs that cause the most friction. The roadmap is chosen by evidence, not by a feature catalogue.
One accountable team
Design, build, hosting and support sit under one roof. When something crosses a boundary, there is no vendor ping-pong and no gap where responsibility quietly disappears.
Evidence-first delivery
Every two weeks there is a deployed release and a short written note on what moved, what did not and why. The measures are agreed up front - task accuracy and human review time - and reviewed after launch.
A clean handover
Code, accounts, documentation and runbooks are transferred to you. Support is a choice you make, not a dependency we design into the system.
Practical compliance
Controls are written into the schema and the interface, with awareness of DPDP Act 2023 and EU AI Act expectations. We do not sell certification, and we do not pretend software alone creates compliance.
What you receive
Built for you.
Ready to be yours.
Scope and success measures
One page that says what ships in core ai, what does not, and how task accuracy and human review time will be measured against a baseline.
Workflow and handoff map
A practical map of how prompts moves between operations, data and support teams, including the exceptions that never appear in a requirements document.
A working first release
A deployed release covering the core flow, with a staging link, a short walkthrough and a rollback path that has been tested.
Data migration and reconciliation
A dry run, a reconciliation report and a read-only window on the old system, so historical prompts remains answerable after switchover.
Integration notes
Documented connections to a vector store and the workflow engine, with credentials, failure modes, retry behaviour and an owner for each.
Roles and permission matrix
A readable matrix of who can view, edit, approve and export, including the break-glass path for urgent exceptions.
Operational runbook
Setup, deploy, backup, restore and rollback steps written for your team, including the small details that usually live in someone's head.
Role-based training
Short sessions for operations, data and support teams, with recorded walkthroughs and a one-page cheat sheet per role rather than a generic manual.
Documentation library
Architecture notes, data dictionary, API references and decision records, organised so a new hire can find the reason behind a choice.
Audit and compliance evidence
Access logs, approval trails and retention settings aligned with DPDP Act 2023, presented in a form your team can hand to a reviewer.
Handover pack
Code repository, environments, domains, credentials and third-party accounts transferred with a checklist both sides sign.
Support plan
A named contact, a response window and a short feedback loop, with monitoring and backup checks included if you choose ongoing support.
Business value
Designed to make a difference.
Discovery
Treated as a success measure from the first workshop, then reviewed again four weeks after launch and documented against the baseline.
Build
Tracked in the same operational report the team already reads, so the improvement is visible without a separate study.
Integrate
Written into the handover notes with the baseline, the measurement method and the person who owns the review.
Less manual rework
Fewer retyped prompts and fewer reconciliation exercises, because the same record drives the next step instead of being copied into it.
Clear ownership
Each handoff has a named owner and an escalation path, so a stalled item is visible before it becomes a customer complaint.
Reporting you can trust
Daily and weekly views built from the operational record, so task accuracy is available during the review rather than after it.
Easier audits
Access and approval history for prompts is attributable and retained, which shortens internal reviews and external questionnaires.
Lower key-person risk
Documentation and runbooks reduce the number of processes that only work because one person remembers how.
The intended outcome
Core AI built for Indian businesses, with one accountable team.
Scope, build and run core ai with measurable outcomes - no vendor handoffs, no black boxes.
- Discovery
- Build
- Integrate
Security & compliance
Confidence is
part of the build.
Role-based access with per-module permissions and an audit trail for every view and edit of prompts.
Encryption in transit and at rest, with a documented key-management and retention process aligned with DPDP Act 2023.
Consent and data-sharing rules that reflect how prompts is collected, used and shared across teams.
Configurable retention for prompts and retrieved context, including a defensible deletion process.
Separation of duties for sensitive approvals, with break-glass access that is logged and reviewed.
Backups, restore drills and a written incident-response path, rather than an untested assumption that recovery works.
Awareness of EU AI Act expectations and sector-specific AI guidance, applied to the scope of the system rather than claimed as a certification.
How do we start?
A 30-minute call and a written scope before any work begins.
How long does a first release take?
Most engagements ship a working first release in 4-6 weeks. The exact window depends on how many systems need to connect and how clean the existing prompts is. We agree the first release in writing before work starts.
What does an engagement cost?
Most first releases are a fixed fee with a fixed scope, agreed after the discovery workshop. Later phases are priced separately so you can stop, continue in-house or bring in another partner without renegotiating the whole programme.
Can you work with our existing systems?
Yes. We start with a short audit of an LLM API, a vector store, the workflow engine and the data that moves between them. Where an integration is missing, we document the workaround and decide whether to build a connector, change the process or leave it alone.
Who owns the code, accounts and data?
You do. Repositories, cloud accounts, domains, credentials and third-party subscriptions are transferred at handover, along with the documentation needed to operate them without us.
How do you handle data migration?
We inventory the sources, define merge rules for prompts, run a dry-run import, reconcile counts with your team and only then switch over. The old system stays read-only for a defined period, so an old record can still be checked.
How involved does our team need to be?
Expect one working session per week with the people who own the process, plus a short review at the end of each increment. The operations lead who uses the system every day is the most important reviewer, not the steering committee.
Can you host and support it after launch?
Yes, on a monthly retainer with a defined response window, monitoring, backups and a named contact. Support is optional; the handover is designed so you are not locked in.
What happens after launch?
We review task accuracy and human review time against the baseline, fix the issues that block adoption, and agree a short list of next modules. The next phase is chosen by measured value rather than by the original roadmap slide.
Why not buy an off-the-shelf product?
If a product fits the way operations, data and support teams work, we will say so. A custom build makes sense when the workflow is a competitive advantage, when integration is the hard part, or when the available tools force a process your team does not actually want.
Let's work together
Let's talk core ai.
Tell us what you are building or what needs to work better. We will help you define the next step.