AI Engineering Partner

Your competitors
are already
shipping AI.

Dwayo integrates production AI into your existing stack — Postgres, Python, AWS, whatever you're running — without a rewrite, without a 7-month hiring cycle, and without a system you can't own or audit.

Book a free 30-min call → See what we've fixed ↓

What we do

Everything you need.
One unified team.

AI Consulting & Strategy

We audit your business, identify high-leverage AI opportunities, and build a phased roadmap grounded in ROI — not hype. No vague frameworks, just execution-ready plans.

AI Audits Roadmapping Use-case scoping Build vs. buy

End-to-End AI Product Dev

From prototype to production. We design and engineer full AI products — LLM applications, agents, RAG systems, and custom model fine-tuning — built to survive real-world scale.

LLM Apps Agents & Automation RAG Systems Fine-tuning

Guardrails & evals built-in

AI Infrastructure & MLOps

Reliability and scale are non-negotiable. We build the pipelines, monitoring, CI/CD, and infra that keeps your AI system performant, observable, and cost-efficient in production.

ML Pipelines Model Serving Monitoring Cost Optimization

Privacy-first architecture

Voice AI Systems

Conversational voice agents that handle real customer calls — understanding intent, managing dialogue flow, escalating to humans when needed. Built for production telephony, not demo environments.

Conversational AI Telephony Integration Intent Detection TTS / STT

Human escalation guardrails

Vision AI & Edge Deployment

Custom computer vision models trained on your specific domain — deployed on edge hardware, embedded systems, or cloud inference. From prototype to grading line without the latency of a cloud round-trip.

Custom CV Models Edge Deployment Quality Grading Real-time Inference

Runs fully on-device

Hard-won experience

What we've seen break.

Not case studies. Specific failure modes we've diagnosed and fixed — the kind that don't show up until production.

RAG pipeline returning confident wrong answers

Root cause: no retrieval eval layer. The vector search was returning plausible but semantically off chunks. The LLM hallucinated confidently on top of bad context.

Fix → Retrieval precision/recall benchmarks + chunk-level confidence scoring before any answer is surfaced.

LLM agent taking unintended actions in production

Root cause: no output schema enforcement on tool calls. The agent was given a list of actions it could take. With ambiguous prompts, it chose the wrong one — at scale, automatically.

Fix → Strict output schemas + action whitelists + human-in-the-loop gates on all write operations.

Inference costs 40× higher than projected at scale

Root cause: nobody modelled token usage at volume. A feature that cost $0.002 per call looked fine in staging. At 500k calls/day it was a $30k/month surprise.

Fix → Token budget modelling in the architecture phase + cost dashboards from day one of production.

Model provider API change breaks entire product

Root cause: direct coupling to one provider's API throughout the codebase. When they deprecated a parameter, every call broke simultaneously. The fix took three weeks.

Fix → Provider abstraction layer from day one. Swap models in config, not in 200 files.

Voice agent confidently answers questions it wasn't trained for

Root cause: no out-of-scope detection. The agent was given a knowledge base and told to answer questions. It extrapolated freely — giving confident wrong answers on topics outside its domain. Customers escalated angry, not confused.

Fix → Intent boundary classifier before the LLM layer. Unknown intents route to human, not to the model.

Vision model that aced the lab but failed on the line

Root cause: training data was clean studio images. Production camera had variable lighting, motion blur, and lens distortion. Model accuracy dropped from 94% in testing to 61% in the field. Nobody had tested on real hardware.

Fix → Always train on real deployment hardware. Augment with lighting variance, motion, and sensor noise from day one.

How we work

Structured for speed.
Built for depth.

Stack Audit & Architecture Review

We map your existing infrastructure — databases, APIs, data pipelines, auth systems — and identify exactly where AI slots in without breaking what works. You give us access. We give you a gap analysis.

↳ Deliverable: Stack map + integration risk doc

Week 1

Technical Blueprint & Trust Spec

A precise architecture doc: system diagrams, model selection rationale, data flow maps, cost projections, and a written Trust Spec covering privacy, guardrails, and compliance — before a single line of code.

↳ Deliverable: Architecture doc + Trust Spec PDF

Week 1–2

Prototype → Your Engineers Review It

A working proof of concept against your real data. Your engineers review the code, ask questions, push back. We iterate. No black boxes — your team can read everything we write.

↳ Deliverable: Reviewed, commented prototype + eval baseline

Week 2–4

Production Build, Hardening & Evals

Full system built with logging, eval suites, guardrails, fallback logic, and CI/CD. Every milestone has a benchmark. Nothing ships without passing a defined quality threshold.

↳ Deliverable: Production system + eval dashboard

Week 4–10

Handoff: You Own Everything

Full source code, infra configs, runbooks, and a live knowledge transfer session with your team. IP transferred in writing. You can operate this without us from day one of handoff.

↳ Deliverable: Full codebase + runbook + IP transfer agreement

Week 10–12

Client outcomes

What we've actually delivered.

Clients are kept anonymous by agreement — but outcomes are real, documented, and verifiable on a call.

SaaS · B2B · Series A

Reduced support ticket volume by 64% after deploying an LLM-powered triage and auto-resolution layer on top of their existing Zendesk + Postgres stack. No data migration. Live in 5 weeks.

RAG Pipeline Zendesk Integration 5 weeks

FinTech · Lending · Scale-up

Built a document intelligence system that extracted structured data from unstructured loan applications with 94% accuracy — replacing a 12-person manual review team for first-pass triage.

Document AI Structured Extraction 8 weeks

E-commerce · Marketplace · $40M ARR

Shipped a personalised AI recommendation engine integrated directly into their existing Django backend. Avg order value up 22% within 60 days of launch. Inference cost held under $800/month at 2M daily requests.

Recommendation Engine Django Integration 10 weeks

Telecom · B2C · Scale-up

Built an AI voice agent handling inbound customer queries over phone — intent classification, dynamic dialogue management, and CRM integration. Deflected 58% of calls from human agents within 30 days of go-live. Average handle time for escalated calls dropped by 34% because the agent pre-qualified context before handoff.

Voice AI Whisper STT Twilio Integration 7 weeks

AgriTech · Hardware · Early-stage

Prototyped a fruit grading system using a custom vision model deployed on edge hardware — classifying produce by size, colour consistency, and surface defect with 91% accuracy at line speed. Ran entirely on-device with no cloud dependency, enabling deployment in low-connectivity environments.

Vision AI Edge Deployment Custom CV Model 6 weeks

Note

Specific company names, team contacts, and supporting metrics are shared under NDA on discovery calls. We don't publish client names without written permission — but we don't expect you to take outcomes on faith either.

Who you're working with

Not a consultancy.
An engineering team.

Devji Chhanga

Co-Founder & AI Engineering Lead, Dwayo

10+ years building scalable systems across cloud infrastructure, ML/AI, Big Data, and NLP. Shipped production-grade solutions for startups and enterprise clients — from distributed data pipelines on Apache Spark to LLM-powered products. Founded Dwayo because most companies have great engineers who haven't shipped AI in production yet — and that gap is closeable with the right team alongside them.

10+ years engineering ML · NLP · Big Data · LLMs Cloud-native systems

linkedin.com/in/idevji →

We tell you when we're not the right fit. If your problem doesn't need AI, we'll say so. We've turned down projects that weren't ready — and those clients came back when they were.

We write code your team can read. Every PR is documented. Every architecture decision is explained. We build systems that your engineers can own — not ones that require our continued involvement.

We price by milestone, not by hour. You know what you're getting before you sign. No scope creep invoices. No "that's out of scope" surprises mid-project.

Trust & Safety

AI that your legal team
won't flag.

We know the real blocker isn't budget — it's fear. Fear of a hallucination embarrassing your company, a data leak, or a system you can't audit or control. We treat these as engineering problems with engineering solutions.

🔒

Privacy

Data Privacy & Confidentiality

Your data never trains a third-party model. We architect data flows with strict isolation — what goes in stays in your environment.

No data leakage to public model providers by default
On-premise or VPC deployment options for sensitive workloads
PII redaction pipelines before any LLM interaction
Audit logs on every inference call, queryable at any time

🛡

Reliability

Hallucination & Output Control

Generative AI is probabilistic — but unreliable outputs are an engineering failure, not an inevitability. We build eval frameworks that catch bad outputs before users do.

Automated eval suites run on every deployment
Confidence scoring & fallback logic for uncertain outputs
Human-in-the-loop checkpoints for high-stakes decisions
Output schema enforcement — structured, validated responses

⚖️

Compliance

Regulatory & Compliance Risk

GDPR, SOC 2, HIPAA, the EU AI Act — regulatory surface area is growing fast. We design systems with compliance baked in, not bolted on after the fact.

Data residency controls — processing stays in your required region
Model cards & documentation for regulatory audit trails
Bias & fairness testing as part of every eval pipeline
Right-to-explanation architecture for regulated decisions

🔓

Control

Vendor Lock-in & Loss of Control

We don't build systems that only work if you keep paying us — or if OpenAI changes their API. Portability and ownership are design constraints, not afterthoughts.

Model-agnostic architecture — swap providers without rebuilding
Full source code & IP ownership transferred at handoff
Comprehensive runbooks so your team can operate independently
No proprietary black-box wrappers — open, auditable stack

The honest question

Why not just hire in-house?

It's the right question. Here's the honest answer.

Dwayo

In-house ML hire

Time to first production feature

6 weeks

6–9 monthshiring + onboarding + ramp

Annual cost

Project-basedno benefits, no equity, no HR overhead

$280–420Ksalary + benefits + equity + recruiting

Who owns the IP

You. Fully.transferred in writing at handoff

You

Coverage

Strategy + Product + MLOps

One specialityyou still need a team around them

Risk if it doesn't work out

Lowmilestone-based, defined exit points

Highseverance, rehiring, 6-month gap

Can operate without you after

Yes — by design

Yes

In-house is the right answer eventually — once you know exactly what you're building and need someone to own it long-term. We get you there faster, at lower risk, and hand over everything when you're ready.

Your competitors are already shipping AI.

Everything you need.One unified team.

What we've seen break.

Structured for speed.Built for depth.

What we've actually delivered.

Not a consultancy.An engineering team.

AI that your legal teamwon't flag.