Process Discovery
Task and process mining to find where time actually leaks, ranked by effort against annual saving.
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ServicesDeep expertise, delivered as outcomes.
Six practices, one delivery standard. Engage us for a single specialist, a full pod, or end-to-end ownership of a platform — the quality bar doesn't move.
01 — Fintech Engineering
Money movement that survives scale, scrutiny and 3 a.m.
We build the parts of financial systems that cannot be "mostly right": ledgers, settlement, limits, reconciliation and the audit trail behind them.
- Payment orchestration across UPI, cards, wallets, NEFT/RTGS and ISO 20022 rails
- Double-entry ledgers, settlement, chargebacks and dispute workflows
- Lending origination, credit decisioning and collections platforms
- KYC/AML, sanctions screening and transaction-monitoring pipelines
- Core-banking integration, middleware modernisation and mainframe offload
- PCI-DSS-aware architecture, tokenisation and key management
Ledger Core
Immutable, reconciliable, provably balanced.
Real-time Risk
Sub-100ms decisioning at peak volume.
Recon Engine
Daily close in minutes, not days.
RegTech
Reports regulators accept the first time.
02 — Cybersecurity
Assume breach. Then engineer accordingly.
Security that lives in your pipeline and your architecture, not in a PDF that ages badly.
- Threat modelling, secure architecture review and ADR-level guidance
- Application, API, cloud and container security assessments
- VAPT, red-team exercises and remediation partnership until closure
- Zero-trust design, IAM/PAM rollout and secrets management
- SOC enablement, SIEM/SOAR tuning and detection engineering
- DevSecOps: SAST, DAST, SCA, SBOM and policy-as-code in CI
- Compliance readiness — ISO 27001, SOC 2, RBI, GDPR, DPDP Act
# Trinetra · security gates that block the merge stages: - sast: tool: semgrep fail_on: high - secrets: tool: gitleaks fail_on: any - deps: tool: trivy fail_on: critical - iac: tool: checkov fail_on: high - sbom: format: cyclonedx sign: cosign - dast: target: staging profile: owasp-top10 policy: waiver_requires: ["security-council", "expiry-date"] # no permanent exceptions. ever.
03 — Automation & RPA
Bots that pay for themselves — and we'll show the maths
We start with process mining and an ROI model. If automation isn't the right answer, we say so before you buy licences.
Attended & Unattended Bots
Production bots with exception handling, retries, credential vaulting and human-in-the-loop escalation.
Intelligent Document Processing
OCR plus LLM extraction for invoices, KYC packets, claims and contracts — with confidence thresholds.
Workflow Orchestration
Long-running business processes modelled properly — Temporal, Camunda or cloud-native state machines.
Agentic Automation
Where rules run out, LLM agents take over — with tool permissions, budgets and audit logs.
Centre of Excellence
We stand up your internal automation CoE — standards, reusable components, governance and training.
04 — AI/ML, RAG & Agents
AI that cites its sources and knows when to stop.
Anyone can wire an LLM to a chat box. The hard parts are retrieval quality, evaluation, guardrails and the operating cost at month twelve. That's our work.
- Enterprise RAG: chunking strategy, hybrid search, rerankers and citation grounding
- Multi-agent systems on LangChain and LangGraph with explicit state and human checkpoints
- Fine-tuning, distillation and small-model strategies to cut inference cost
- Evaluation harnesses — golden sets, LLM-as-judge, regression gates in CI
- Guardrails: PII redaction, prompt-injection defence, output schemas, refusal policy
- Classical ML — forecasting, fraud detection, churn, recommendation, computer vision
- LLMOps: prompt versioning, tracing, cost dashboards, drift and hallucination monitoring
0Avg. Support Deflection
0Retrieval Precision Gain
0Inference Cost Cut
0Answers with Citations
Our non-negotiables for AI in production
No unevaluated model ships. No answer without provenance. No agent with unbounded tool access. No PII leaving your boundary without an explicit, logged decision.
05 — Data Engineering
Your AI is only as honest as the data beneath it.
Pipelines, contracts, lineage and governance — the unglamorous layer that decides whether everything above it is trustworthy.
- Lakehouse and warehouse builds on Databricks, Snowflake, BigQuery or Redshift
- Streaming and CDC pipelines with Kafka, Debezium, Flink and Spark
- Orchestration and transformation with Airflow, Dagster and dbt
- Dimensional and data-vault modelling plus a governed semantic layer
- Data quality, contracts, lineage and catalogue with automated tests
- Migration from legacy ETL and on-prem warehouses without a big-bang cutover
- Vector stores and feature stores wired for both ML and RAG workloads
# Trinetra · data contracts enforced at ingest from trinetra.data import Contract, expect payments = Contract( name="payments.v3", owner="platform-payments", sla_minutes=15, checks=[ expect.not_null("txn_id", "amount_minor"), expect.unique("txn_id"), expect.range("amount_minor", min=1), expect.enum("status", ["pending", "settled", "failed"]), expect.freshness("ingested_at", max_lag="15m"), ], on_violation="quarantine_and_page", # never silently drop )
06 — Product & Platform Development
Full-stack engineering in whatever your stack is
Python, C#/.NET, Java, Go, TypeScript, Rust, Kotlin, Swift, PHP, Scala — we staff to the problem, not to whatever our bench happens to know.
Backend & APIs
Microservices, event-driven systems, gRPC and REST, GraphQL federation.
Frontend
React, Next.js, Angular, Vue — accessible, fast, design-system driven.
Mobile
Native Swift and Kotlin, plus Flutter and React Native where it fits.
Cloud & DevOps
AWS, Azure, GCP, Kubernetes, Terraform, GitOps, FinOps and SRE practice.
Legacy Modernisation
Strangler-fig migrations off monoliths and mainframes — incrementally, safely.
QA & Test Automation
Unit through E2E, contract testing, performance and chaos engineering.
UI/UX Design
Research, prototyping and design systems that engineering can actually build.
Managed Support
L2/L3 support, SLOs, on-call and continuous improvement after go-live.
Engagement Models
Work with us the way that suits you
Dedicated Pod
A cross-functional team — engineers, QA, DevOps, a tech lead — embedded with your product owner. Monthly rate, full transparency, scales up or down with 30 days' notice.
Fixed Outcome
Well-defined scope, fixed price, fixed date. We take the estimation risk — which is why we insist on a paid discovery sprint before quoting.
Specialist on Demand
Need one principal security engineer, one LLM evaluation expert or one data architect for six weeks? Take exactly that, with no minimum team size.
FAQ
Questions we get asked before signing
A specialist typically starts within 7–10 working days. A full pod usually takes two to three weeks, because we assemble it around your problem rather than assigning whoever is free.
You do — completely, from the first commit. Code lives in your repositories under your licences. Where we reuse Trinetra components, you receive a perpetual, transferable licence in writing.
NDAs before discovery, least-privilege access, hardware and network controls on our side, and data residency honoured where regulation demands it. For sensitive work we operate entirely inside your environment with no data egress.
Most of our engagements are exactly that. We pair, review, document and deliberately transfer knowledge — the measure of a good engagement is that you need us less over time, not more.
Pods flex with two weeks' notice. Fixed-outcome work uses a written change process with re-estimation. We would rather renegotiate honestly than deliver something you no longer need.
Yes, and we do it often. It begins with a two-week technical audit: architecture, code health, security posture and test coverage, followed by a blunt written assessment and a stabilisation plan.
Next Step
Bring us your hardest problem
Thirty minutes with an engineer. No deck, no discovery theatre.