Straight answers about AnAr, before you talk to us.
What we build, how we work, who owns the code, and how teams are structured. If your question is not on this page, a short note reaches an engineer who can answer it about your system, not a sales queue.
Who we are, and where we work
What is AnAr Solutions?
An AI-native engineering team that builds and runs production software for companies around the world. AI-native means two things in practice: AI runs inside our own delivery, estimation, testing, and support, and we build AI into the systems our clients ship. We are not a reseller of someone else's tools, and not a low-cost body shop. More on the team and how we work: About AnAr →
How long has AnAr been in business?
Since 2014, so 12 years of continuous delivery. We have shipped 150+ projects and keep 95% of clients year over year. One UK insurance-analytics firm has run its product and services engineering team with us for eight straight years.
Where are your teams based?
Our engineering base is in Pune, India, with a US legal entity in Dover, Delaware. We work remote-first with clients across the US, UK, and Europe, and structure hours around each client's overlap window so a real person is reachable during your working day.
Which industries do you work in?
Three deep verticals, each with production systems behind it:
- FinTech and financial services, including an agentic platform that processes 11,000+ investment opportunities a month for an enterprise client. FinTech →
- HealthTech, including HIPAA and HL7-compliant clinical systems and a 340B drug-pricing compliance platform. HealthTech →
- Manufacturing, including a German manufacturer's ERP modernization with SAP integration deployed across international offices. Manufacturing →
Can you show examples of work you have shipped?
Yes. A few, anonymized:
- An enterprise financial-services client uses an agentic AI platform we built to process 11,000+ opportunities monthly and generate outreach automatically.
- A US education-finance company's legacy portal, modernized to a cloud-native stack, now runs at 99% uptime with 30% fewer production support tickets.
- A UK insurance-analytics firm has run its product and services engineering team with us for eight years, across successive versions of their platform.
Full write-ups: Case studies →
The work, and the technology behind it
What services does AnAr offer?
Five areas, all delivered by the same engineering team, not separate practices:
- Agentic AI: agents that run enterprise workflows in production, with governance built in. Agentic AI →
- Product engineering: building and scaling software products from first build through the next investor round. Product Engineering →
- Application modernization: moving legacy systems to modern stacks, and rebuilding older apps around AI. AI-Driven Modernization → · Legacy Modernization →
- Cloud services: architecture, migration, and operations on Azure, AWS, and GCP. Cloud Services →
- Maintenance and support: taking over and running existing applications so they keep working. Application Maintenance →
What is the "AI" part, specifically?
Named capabilities, not slogans. Agent orchestration for multi-step enterprise workflows. Retrieval-augmented generation over your own documents and data. Voice AI agents in production. And two accelerators we built and run in-house: an Automation Testing Framework that generates and runs tests from a working application, and a Business Rule Extractor that reads source code and recovers the business rules buried in it. AI Accelerators →
We have an AI proof-of-concept that stalled. Can you get it to production?
This is a lot of what we do. A demo that works in a notebook and a system that holds up in production are different engineering problems, and the gap is rarely the model. It is retrieval quality, integration with the systems you already run, handling of edge cases, security, and monitoring. We take stalled POCs, find where they break under real data and real load, and rebuild the parts that were never production-grade. AI-Driven Modernization →
What happens when the AI gets something wrong?
We design for it, because it will. That means human-in-the-loop review on decisions that carry risk, fallback paths when a model is unsure, confidence thresholds that route edge cases to a person, and logging so every AI decision is traceable after the fact. In regulated work, that audit trail is not optional. We do not ship an AI feature that has no answer for the day it is wrong.
What technologies do you work in?
Backends in .NET Core, Python, Node.js, and Java. Frontends in Angular and React. Cloud on Azure, AWS, and GCP. Data on SQL Server, MySQL, and MongoDB. For AI work, agent orchestration, RAG pipelines, and voice interfaces on top of current frontier models. We pick the stack to fit the system, not a house favorite.
Do you have real cloud experience, or just certifications?
Real delivery on Azure, AWS, and GCP: migrations off on-premise and legacy hosting, re-architecture for scale, and ongoing operations. One manufacturing client's ERP was migrated to Azure with SAP integration and rolled out across international offices. Cloud Services →
How a project actually starts and runs
How do I start a project with AnAr?
Send a short note through the form on this site describing the system and what you want to move. An engineer reads it and replies within one business day with first thoughts, not a sales script. If it looks like a fit, we set up a scoping review to look at the system together and agree what the work involves. That review is the start of the work, not a pre-sales pitch.
How quickly can we get started?
Fast. A scoping review usually happens within days of your first note, and a dedicated team can be assembled and productive in a couple of weeks rather than a couple of months, because hiring and onboarding run in-house. For a contained AI build, we aim to put a working prototype in front of you inside the first few weeks, not after a long discovery phase.
How do you deliver? Is there a method?
Every engagement runs on one method: Understand, Plan, Implement, Verify. We map the system before touching it, agree a plan, build in reviewable increments, and verify against real behavior, not just a green test suite. The same discipline is what lets an AI-assisted team ship code that still holds up six months later. AI-Assisted Development →
How do you keep delivery on time?
Iterative delivery with short review cycles, so problems surface early instead of at the deadline. We commit to dates we can hold, and if a scope change moves one, you hear it as it happens. Where a date depends on inputs from your side, we flag that dependency at the start, not after a slip.
How do you handle quality and testing?
Testing at every stage: unit, integration, and system, with test-driven development where it fits. Our own Automation Testing Framework generates and runs tests against the real application, including inherited, messy, production codebases, which is where quality problems actually live. AI Test Automation →
How do you price engagements?
By the shape of the work, not a rate card. Project-based work runs fixed-price when the scope is firm, or time-and-material when it is still moving, so you are not paying for guesses. A dedicated team is billed as a monthly team, so your spend stays predictable as the roadmap moves. The scoping review sets the model and the estimate before you commit, so there is no surprise once the work starts.
How teams are structured and scaled
Can AnAr act as our dedicated engineering team?
Yes. Many of our engagements are dedicated teams that work as an extension of your organization, in your tools and your rituals, reporting where your own engineers report. The model is continuity: senior engineers who stay on your system long enough to know it well. Global Team Solutions →
Do you work with startups, or only larger companies?
Both. Engagements range from an early-stage product company adding its first AI feature to an enterprise running a platform across global offices. You do not need a large budget or a finished spec to start. A contained pilot is a normal first step, and it can grow into a standing team once the work proves out.
Can you work alongside our in-house engineers?
Yes. Teams embed with your existing engineers rather than working in a silo. Shared repositories, shared standups, shared code review. The aim is that six months in, it is hard to tell which commits came from which side.
How do we stay in sync across time zones?
Your team works in your tools and your rhythm: your repositories, your issue tracker, your standups. We set working hours to overlap with your day so there is live time for reviews and quick decisions, and communication is direct with the engineers doing the work, not routed through an account manager.
How do you scale a team up or down?
We start by sizing the team to the work, then grow or shrink in planned steps as the roadmap changes. Because hiring and onboarding run on in-house recruitment, not a scramble for contractors, added engineers come up to speed on your system fast. Global Team Solutions →
What engagement models do you offer?
Two ways to engage. Project-based, where we take a defined scope and deliver it end to end. Or a dedicated team that runs as an extension of your organization for ongoing product and platform work. Many clients start project-based and move to a standing team once the work proves out. How each is priced is set at the scoping review. Global Team Solutions →
Who owns the code, and how it is protected
Who owns the code and IP?
You do. Source code and intellectual property belong to the client. That is agreed in writing before any code changes hands.
How do you protect confidentiality?
Confidentiality terms are signed before we see your code, and visibility is limited to the engineers assigned to your engagement, not the wider company. Your system is not a demo we reuse elsewhere, and it is not named in our marketing without written agreement.
What about compliance and data protection?
We build to the regime your product lives under, including GDPR and HIPAA where they apply, and have shipped HIPAA and HL7-compliant clinical systems and a 340B compliance platform in healthcare. Encryption in transit and at rest, controlled account privileges, and audit trails are part of how we build, not an add-on.
How do you keep the software itself secure?
Secure coding practices, dependency and vulnerability review, and security testing inside the delivery cycle rather than bolted on at the end. When we take over an existing system, a security and reliability review is part of the handover.
Why teams choose AnAr
We build the tools, and we run them.
The AI accelerators and delivery method are built and operated by the same engineers who maintain and modernize client systems, not sold on from a vendor.
AI-native, in our own delivery first.
AI runs our estimation, testing, and support before it touches your system, so we know what holds up in production and what does not.
150+ projects. 95% client retention. 12 years of delivery.
Clients stay with us for years because the systems we touch keep working. The proof is in how long the relationships last.
Your code stays yours.
Source and IP are protected. Confidentiality is agreed in writing before any code is shared, and visibility is limited to the engineers on your engagement.
Question not answered here?
A short note is enough. Tell us about the system or the problem, and an engineer comes back with a real answer, not a sales reply.
People usually reach out about:
- An AI idea or a stalled proof-of-concept that needs to reach production
- A legacy system that has to be modernized or taken over
- A product that needs a dedicated engineering team to build and run it
- A specific question about your stack, timeline, or how we would engage
You will speak with engineering, not sales. The first reply comes from someone who can answer technical questions about your system, within one business day.
Send a Message
A few details so the right engineer follows up.
Routed to the right engineer
We read what you sent and route it to someone who works in that area, not to a sales queue.
A reply within one business day
You get a substantive response from an engineer, with first thoughts on your system where they are clear.
A review to go deeper
We set up time to look at the system and agree what the work would involve.
Want the fuller story on the team? See About AnAr →
Prefer to see proof first? Read the case studies →
Ready to talk specifics? Contact us →