FLAGSHIP
Agentic RAG Studio
Production-ready RAG over your private corpus. Hybrid search, evals, fallbacks, and per-tenant grounding — all wired in.
- 6-week deploy
- Evals from day one
- Multi-tenant aware
Now booking Q3 outcome pods
Software services, redesigned for the AI era.
AI-native pods that ship outcomes — not timesheets. We modernize legacy systems, build AI-native products, and run production AI at the velocity your traditional IT vendor can't match.
450+
Engineers in the pod network
96%
Client retention
20+
Countries served
10+
Years of partner-delivered work
// Stats reflect the combined Swiftnex × Techmango partner delivery network.
Trusted across the partner network
IKEA
Honeywell
Braun
Tech Mahindra
TVS
TVS Eurogrip
Prime Healthcare
Coats
National Life
Phifer
KyckGlobal
Livingston
IKEA
Honeywell
Braun
Tech Mahindra
TVS
TVS Eurogrip
Prime Healthcare
Coats
National Life
Phifer
KyckGlobal
Livingston
What we do
Six service lines. Every one AI-native.
Built around how modern engineering actually ships in 2026 — not the resource-pyramid model that ran IT services for 25 years.
01 / 06
AI-Native Product Engineering
Ship AI-first products end to end — from problem framing to production agents, RAG systems, and copilots that actually move metrics.
- Agentic apps & copilots
- Production RAG with evals
- Multimodal experiences
- Foundation-model fine-tuning
Learn more
02 / 06
Legacy-to-AI Modernization
We rip out the brittle middle layer of legacy stacks and rebuild it AI-native — with code-aware agents reading 20-year-old codebases so humans don't have to.
- Mainframe & monolith refactor
- AI-assisted code translation
- Schema & API redesign
- Cutover with parallel-run safety
Learn more
03 / 06
Data Engineering, Reimagined
Pipelines, lakehouses, and feature stores designed for LLMs and agents — not just BI dashboards. Real-time, governed, and AI-ready by default.
- Modern lakehouse builds
- Streaming + CDC pipelines
- Vector + hybrid retrieval
- Data contracts & lineage
Learn more
04 / 06
Platform & Cloud Engineering
Cloud-native platforms that ship on day one — multi-cloud, IaC-first, with secure-by-default guardrails baked into the SDLC.
- AWS, Azure, GCP delivery
- Kubernetes platform builds
- DevSecOps automation
- FinOps + reliability SLOs
Learn more
05 / 06
Applied AI Consulting
Most AI roadmaps die in pilot purgatory. We diagnose what's worth building, what to buy, and what's a model away from useful — and ship the first production system in a quarter.
- AI opportunity audits
- Build/buy/wait scoring
- Production-grade pilots
- Eval + safety frameworks
Learn more
06 / 06
Extended Engineering Pods
When you need bandwidth — not bodies. Senior, AI-augmented pods that plug into your repo, your standup, and your roadmap. No bench, no rotations.
- Embedded full-stack pods
- Shared roadmap ownership
- Code-review & SRE rotations
- Knowledge graphs of your stack
Learn more
AI Accelerators
Productized building blocks. Not blank-page consulting.
Six accelerators we deploy on top of bespoke engineering. Each one is opinionated, version-controlled, and battle-tested across the partner network — so you skip the pilot tax and start at production.
POPULAR
Internal Copilot Kit
Drop-in copilots for ops, finance, and engineering teams. Connect, ground, and ship a measurably useful internal assistant in under 30 days.
- 30-day MVP
- BYO LLM
- Audit-grade logging
NEW
Legacy Decoder
Code-aware agents that read your 20-year-old codebase, generate specs, surface dead code, and write the migration plan. We do the actual port.
- COBOL → modern stack
- Spec-first migration plan
- Parallel-run validation
PROVEN
AI-Ready Data Platform
An opinionated lakehouse build with vector retrieval, feature store, and governance pre-wired. From greenfield to first model in 12 weeks.
- Lakehouse + vectors
- Lineage + contracts
- 12-week first model
ON BY DEFAULT
AI-First SDLC
Spec-driven dev, secure pre-commit guardrails, AI code review, and eval-led QA — installed in your repos in a week.
- Spec → tests → code
- Secret + vuln scanning
- Eval gates on PRs
RUNTIME
Agent Ops
Run agents in production without breaking things. Observability, cost guardrails, evals, and human-in-the-loop policies — all instrumented.
- OTEL for agents
- Cost & rate guardrails
- Replay-driven debugging
How we work
Outcome pods. AI-first delivery. No staffing carousel.
Most engagements stall in handoffs and hidden ramp time. Ours don't. Here's the four-phase model that runs every Swiftnex engagement.
01
Outcome framing
We start with the metric, not the ticket. One week to align on the outcome, the success threshold, and what we'd kill the project to avoid.
02
Pod assembly
A 3–6 person pod — senior engineers, an applied scientist, and a delivery lead — wired into your tools by day three. No staffing carousel.
03
AI-first build
Spec-driven dev, AI-augmented coding, secure pre-commit guardrails, and eval gates on every PR. We move fast because the harness catches what we miss.
04
Run + improve
We instrument the outcome, not the output. Weekly metric reviews, eval drift alerts, and a kill-switch on anything that stops working.
// OPERATING PRINCIPLE
“If our AI-augmented pod ships in half the time of a traditional vendor, you should pay for the result — not the calendar.”
— Swiftnex Tech, engagement playbook
Industries
We've shipped here. Recently. In production.
Eight verticals where the partner network has live AI workloads running today — not slideware.
Banking & Financial Services
Multilingual AI assistants, KYC automation, claims intelligence.
Healthcare
HIPAA-grade copilots, multi-request triage apps, ops automation.
Manufacturing
Product traceability, machinery troubleshooting LLMs, plant-floor copilots.
Logistics & Distribution
Supplier payment automation, mail-sorting AI, route optimization.
Retail & Consumer
Personalization, search reranking, agentic merchandising.
Insurance
Claims triage, policy QA, underwriting assistants.
Real Estate
Document intelligence, valuation models, tenant copilots.
Education
Adaptive learning, tutor agents, content generation pipelines.
Selected work
From challenges to triumphs. Outcomes, not anecdotes.
Each of these is a real engagement delivered through the Swiftnex × Techmango partner network. Names redacted where required by the customer.
BankingCASE / 01
Multilingual GenAI assistant for a Middle East bank
Problem
A leading bank serving customers in six languages was buried in repetitive Tier-1 contact-center load.
Work
We deployed a multilingual GenAI assistant grounded in policy docs and account data, with human handoff and audit-grade logging.
Outcome
First-touch resolution lifted in 9 weeks. Tier-1 deflection well above the program's go-live threshold.
GenAIRAGMultilingualBFSI
Talk to the team that shipped this LogisticsCASE / 02
Supplier-payment AI copilot for a global logistics firm
Problem
Manual supplier-payment clearance was a 5-day cycle riddled with reconciliation errors.
Work
Built a Microsoft Copilot + Power Automate workflow tying ERP, banking feeds, and exception queues into one agentic flow.
Outcome
Cycle time compressed to under 24 hours. Exception backlog dropped by an order of magnitude.
CopilotPower AutomateERPAgentic
Talk to the team that shipped this ManufacturingCASE / 03
Product traceability platform for a manufacturing giant
Problem
Recall investigations took weeks because batch-level provenance was scattered across MES, ERP, and quality systems.
Work
Built a unified traceability platform with GenAI-assisted root-cause narratives and a vector search across QA reports.
Outcome
Recall investigation time cut from weeks to days. Root-cause drafts now AI-generated, human-validated.
GenAIIndustrialVector Search
Talk to the team that shipped this Aviation ITCASE / 04
LLM ops bot for global airport operations
Problem
Ground crews and operations leads needed real-time access to 1,000+ pages of SOPs, dispatched across regions.
Work
An LLM-grounded ops bot with retrieval over SOPs, schedules, and incident logs — on mobile, with offline fallback.
Outcome
SOP lookup time fell to seconds. Adopted across multiple airport hubs in the first quarter.
LLMRAGMobile
Talk to the team that shipped this ManufacturingCASE / 05
LLM troubleshooting assistant for safer machinery operations
Problem
A Saudi manufacturer needed a troubleshooting assistant trained on dense, multilingual machinery manuals — used by technicians on the floor.
Work
Fine-tuned retrieval LLM with safety-gated answers, image-based fault recognition, and a strict no-hallucination policy on critical steps.
Outcome
Mean-time-to-resolution down significantly. Safety incidents tracked and audited per query.
LLMSafetyVision
Talk to the team that shipped this SustainabilityCASE / 06
Computer-vision sorter for a UK bottle-recycling startup
Problem
A pioneering UK startup needed real-time bottle classification at line speed across 20+ material types.
Work
Built an edge CV pipeline with onboard inference, retrain loops from production drift, and a metrics dashboard for ops.
Outcome
Sort accuracy crossed the throughput threshold needed for commercial rollout. Now expanding to additional plants.
Computer VisionEdgeSustainability
Talk to the team that shipped this LogisticsCASE / 07
Cloud GenAI platform for a US mail-sorting OEM
Problem
A leading OEM of mail-sorting systems wanted GenAI features built into their cloud product, without bloating existing teams.
Work
Stood up a managed GenAI platform on top of their cloud — multi-tenant, evals-instrumented, with cost guardrails per tenant.
Outcome
First two GenAI features shipped to customers in under 90 days. Per-tenant unit economics modeled and held.
GenAI PlatformMulti-tenantCost
Talk to the team that shipped this HealthcareCASE / 08
Multi-request health app powered by GenAI
Problem
Patients juggled four different apps for triage, scheduling, records, and medication — none of them talked to each other.
Work
Unified GenAI front-door app with intent routing, agentic task completion, and FHIR integration to existing EHR.
Outcome
Time-to-care dropped meaningfully. Patient NPS for first-touch interactions saw a step change.
GenAIFHIRAgents
Talk to the team that shipped this // Outcomes shown are representative of partner-delivered engagements. Swiftnex Tech operates these via our partnership with Techmango.
Why Swiftnex
Six things we won't do that traditional IT services still do.
The IT services industry was designed for a different problem: shipping standardized software at scale with armies of FTEs. The AI era doesn't need that company. It needs this one.
Traditional IT
→
Swiftnex Tech
Staff augmentation by FTE
Outcome pods on a metric
We don't sell seats. We sell a defined outcome with a kill-switch threshold.
T&M billing, padded hours
Fixed outcomes, value-based pricing
If our AI-augmented pod ships in half the time, you pay for the result — not the calendar.
Bench rotations, junior swaps
Senior, AI-augmented pods
No 1:3 junior:senior ratios. Our pods are senior-led, with AI doing the rote work juniors used to do.
Slideware AI strategy
Production AI in 12 weeks
We don't sell roadmaps for a year. First production system in a quarter or we stop billing.
Hand-coded everything
AI-first SDLC with guardrails
Spec-driven dev, AI code review, secure pre-commit hooks, and eval gates on every PR.
Lift-and-shift modernization
Legacy-to-AI redesign
We don't move COBOL to the cloud and call it done. We rebuild AI-native, with code-aware agents reading the legacy for us.
Delivery partner
Powered by a 450-engineer delivery partner — Techmango.
Our delivery partner since day one. 450+ engineers, 10+ years in the trenches, and a 96% client retention rate across 20+ countries. Swiftnex Tech is the AI-era go-to-market — the strategy, productized accelerators, and outcome contracts. Techmango is the production engine: a decade of shipped work, ISO and Microsoft AppSource credentials, and a hardened delivery model.
Visit TechmangoSenior engineers
450+
Countries served
20+
Client retention
96%
Years delivering
10+
Let's talk
Tell us the outcome. We'll tell you if we can ship it in 90 days.
Most discovery calls take 30 minutes. By the end you'll have a shaped pod, a target outcome, and a fixed-price proposal — not a ten-page rate card.