AI that ships,
not AI that demos.
We build the machine, not the slide deck.
Agents, orchestration, retrieval and guardrails — running in production, under load, with someone watching the numbers.
And the software they live inside: multi-tenant SaaS, web and mobile apps, CRM, ERP, PoS and Salesforce.
Fourteen capabilities, one senior team, since 2017.
Every business on the wall is running live.
The same platform, deployed per client: isolated data, their own branding, their own limits and approvers.
White-label it and your own customers run it under your name, on their domain.
Then we operate it with you: cost per tenant, alerting, model upgrades and a monthly quality review.
Most AI never leaves the pilot. We build the part that decides whether it does — evals, guardrails, orchestration and the software around it — and then we stay to run it.
Fourteen capabilities, one senior team.
AI Engineering
Agents, orchestration and retrieval built to survive production — with evals, guardrails and observability from day one.
Product Engineering
The software the AI runs inside: multi-tenant SaaS, web and mobile apps, and the operational systems your business runs on.
Platforms & People
Salesforce delivery, and senior engineers embedded directly in your team when you need capacity rather than a project.
Model-agnostic.
Cloud-agnostic.
OpenAI, Claude, Azure OpenAI, Bedrock, Vertex or open weights — on AWS, Azure or GCP. We pick on your eval set, and keep the layer swappable because the right answer changes every few months.
- 2017
- shipping AI since
- 80%
- of routine cases automated
- <2%
- ungrounded answers, gated
- 14
- capabilities, one team
The part that decides whether it survives contact with users.
Anyone can wire a model to a text box. The difference between a pilot and a system in production is the machinery around it — and this is ours.
blocked 3 releases this quarter · p95 latency 1.8s · $0.004/run
Explicit orchestration
A planner decomposes, specialists do narrow work well, a critic checks the result before anyone sees it. Typed state moves between nodes, so a handoff can be validated instead of hoped over.
Tools with contracts
Every system the agent touches is a typed, permissioned tool with rate limits, idempotency and an audit line. An agent can only do what it was explicitly granted.
Quality as a number
An eval suite built from your real cases gates every release. When a model or prompt changes, you see the delta before your customers do.
Five steps, and a way out after each one.
You are never more than a few weeks from a decision point with something real in front of you. If scoping shows AI is the wrong tool for the job, we will say so before anything is quoted.
- 01
Scope
We map the process, the data and the systems with the people who run them, then write down what to build, what it costs and what it should return. The conversation that starts it costs nothing.
Technical scope, estimate, go/no-go - 02
Prototype
Two to three weeks against your real data, not a sandbox. You get something you can click, plus honest numbers on quality and cost per run.
Working prototype, eval baseline - 03
Harden
Evals, guardrails, permissions, budgets, observability and failure paths. This is the step most teams skip and the reason most pilots never ship.
Eval suite in CI, traces, runbooks - 04
Ship
Staged rollout behind flags, with a human in the loop until the numbers hold. Your team is in the code and the dashboards the whole way.
Production release, dashboards, handover - 05
Operate
Monitoring, cost control, model upgrades and a monthly quality review. Models change under you; somebody has to be watching.
SLA, monthly review, improvement backlog
Ten systems, and what they changed.
Named with our clients' permission, confidential where we are not free to say. The numbers are the client's, not ours.
Different domains, the same shape of problem.
Find the repetitive, judgement-light work sitting between two systems. Automate it. Measure what it returned.
Financial services
Document extraction, risk scoring and support that survives an audit.
Healthcare
Triage, scheduling and records retrieval with permission-aware answers.
Retail & ecommerce
Order tracking, returns and recommendations handled end to end.
Manufacturing
Vision QA, predictive maintenance and supplier document processing.
Logistics
Shipment status, exception handling and proof-of-delivery capture.
Real estate
Lead qualification, viewing bookings and contract summarisation.
Travel & hospitality
Multilingual booking changes, guest requests and fare questions.
Technology & SaaS
Support deflection, internal knowledge assistants, copilots in-product.
Professional services
Proposal drafting, timesheet capture and knowledge retrieval.
Automotive
Dealer enquiries, service reminders and parts lookup by voice.
Education
Admissions assistants, course Q&A and assessment support.
Startups
From prototype to production without hiring a platform team first.
Three ways to start.
Build an AI system, commission custom software, or add senior engineers to your own team. Whichever you pick, the first conversation costs nothing.
Build AI system
most commonAgents, orchestration, automation or retrieval, taken to production
- Dedicated squad with a technical lead
- Evals, guardrails and observability included
- Fortnightly demos against your real data
- Handover with docs, tests and runbooks
Custom development
SaaS platforms, web and mobile apps, CRM, ERP, PoS or Salesforce
- Discovery and technical design
- Senior squad, shipping every two weeks
- Integrations with the systems you already run
- Documentation, tests and team handover
Resources on hire
You need capacity inside your own team and process
- Named senior engineers
- Your tools, your repo, your standups
- Scale up or down monthly
- Technical lead oversight included
“Their AI chatbot revolutionised how we manage customer support. We improved efficiency and dramatically enhanced the customer experience. The integration with our CRM and order systems was seamless, and the savings on support staff costs have been phenomenal.”
“The AI calling app completely transformed how we manage inbound and outbound calls. It understands and responds in real time, handles objections and books appointments. Our team now focuses on closing deals instead of fielding every call.”
“What used to take weeks can now be done in seconds. Our clients are impressed with how fast we can provide quotes, and it has significantly improved our closing rate. The ERP integration was seamless.”
“TheFinansol transformed our entire design workflow. What used to take almost a week can now be done in minutes, and we present more creative options to our clients because of it.”
The things people ask before they sign.
If your question is not here, email us and we will answer it properly rather than route you to a form.
TheFinansol is an AI engineering studio. We build AI agents, multi-agent orchestration, AI workflow automation, RAG and document intelligence, AI chatbots and AI calling systems — and the software around them: multi-tenant SaaS, web and mobile apps, CRM, ERP and PoS systems, and Salesforce delivery. We also place senior engineers directly into client teams.
A prototype against your real data takes two to three weeks. A hardened production system — evals, guardrails, observability, staged rollout — typically lands between six and sixteen weeks depending on scope. Custom development engagements ship a working slice every two weeks from the start.
Build and custom development engagements are fixed scope, quoted after a short scoping conversation, and usually run between a few weeks and four months of a small senior squad. Engineers on hire are priced monthly per person with no placement fee. You get a number before any build starts.
We deploy inside your cloud account or VPC where required, use model providers under no-training agreements or self-hosted open-weight models, keep retrieval permission-aware, and log every tool call for audit. Data residency and compliance constraints are design inputs, not afterthoughts.
OpenAI, Anthropic Claude, Azure OpenAI, AWS Bedrock, Google Vertex AI and open-weight models, on AWS, Azure or GCP. We keep the model layer swappable and evaluate options against your own test set, because the right choice changes every few months.
Yes, and it is a common way we start. We audit the system, instrument it, build an eval suite from its real traffic, and give you a written assessment of what to keep, fix or replace before doing any work.
Yes. Operate covers monitoring, cost control, model upgrades and a monthly quality review under an agreed SLA. Models and providers change underneath you, so someone needs to be watching the numbers.
We are an India-based team serving clients globally, with delighted clients in the USA, Canada, the UK, Germany, France, Italy, Netherlands, Denmark, Dubai, Saudi Arabia, Australia and Singapore. Every engagement gets agreed daily overlap hours in the client's time zone.
Yes. For Orrderlo GmbH we built restaurant PoS, back office and customer ordering apps for Germany with fiscalisation built in: every transaction signed by a certified TSE, KassenSichV-compliant receipts and DSFinV-K exports for tax audits. We build PoS, ERP and CRM systems for other markets with their local tax and invoicing rules in the same way.
Yes. Engineers on hire places named senior engineers — AI, backend, frontend, mobile, Salesforce, DevOps — inside your team and process on rolling monthly contracts, with a technical lead overseeing quality at no extra cost.
Tell us what is slow,
repetitive or expensive.
You will get a straight answer about whether AI is the right tool — including when it is not. A reply comes from an engineer, usually the same day.
- info@thefinansol.com
- phone
- +91 98994 40566
- office
- Infinevo Tech Private Limited · Delhi, India
- message us
