You built the product. Now make it ready for what comes next.
AI-native engineering teams for product companies, from the first build through the next investor round.
What product companies hire us to engineer.
The Round.
A codebase that survives investor diligence. Whether the next round closes in six weeks or six months.
The First Enterprise Logo.
A product that passes a sixty-page security questionnaire on the first read. Whether the deal is on your desk or in your pipeline.
The Inherited Codebase.
A codebase you understand, ship against, and trust within a quarter. After a new CTO, an acquisition, or an outsourced build returned.
The AI Capability.
AI features shipped to your customers, not just used by your engineers. With the eval discipline that holds at production scale.
The MVP, New Product, or Pivot.
A working product in your customers' hands within twelve weeks. Whether you are validating a first idea, launching inside an existing company, or proving a pivot.
If one of these is what you are building, the next step is a conversation.
Talk to an engineering leadTwo things that change what "product engineering" delivers
The code we ship is code your team can still read in six months.
You carry the consequence of every shipped change long after we leave. AI accelerates the right direction and the wrong direction equally, which makes that consequence bigger, not smaller. Our four-phase process (Understand, Plan, Implement, Verify) keeps AI inside a discipline that the codebase, the audit trail, and the future engineer can all live with.
Understand the codebase and the requirement before changing anything. Plan the simplest architecture that holds for 12 months. Implement with traceable, audit-ready commit history. Verify against five independent lenses: functional, regression, security, performance, maintainability.
See the full processAI capabilities go inside the product, not just inside the dev team.
When your product needs an agent, a retrieval layer, a voice interface, or a model-backed feature, the same engineering team that built the rest of the product builds and operates it. The four patterns and what each one involves are below.
The first reason changes how fast your product gets built. The second changes what your product can do.
The AI patterns we build into products
Most "AI inside the product" work falls into one of four patterns. Each has a different engineering shape and a different operational risk.
Your product needs to answer questions grounded in your customer's data RAG (Retrieval-Augmented Generation)
The product needs to surface answers or generate outputs from the customer's documents, transactions, or knowledge base.
Your product needs to take action on your customer's behalf, not just respond Agentic workflows
Multi-step research, summarization, outreach generation, decisioning.
Your product surface is voice, not screen Voice and conversational interfaces
A conversational product interface that handles real-world voice exchanges, with fallback paths when the AI cannot continue.
A single screen or workflow needs a model behind it Model-backed features
Classification, extraction, summarization, or generation, embedded into one part of the product.
Across all four, evaluation is the discipline that holds the work together. Every AI build we ship carries a real eval set, run before merge, that catches the regression a unit test cannot.
Two ways to engage, and how to choose
Most product engineering work runs in one of two shapes. The right shape depends on whether your scope is well-defined or still evolving.
Dedicated engineering team
A cross-functional team operates as part of your engineering organization. You own the roadmap and the priorities. We own the engineering execution. Typical composition: senior + mid engineers, a tech lead, an embedded QA engineer, optional designer and PM. The team scales up or down by sprint.
Fixed-scope project
Defined SOW, fixed timeline, milestone-based delivery. Clear acceptance criteria. Tight handover at the end. MVPs and pivots get the same shape memo, the same engineering discipline, and the same senior engineer commitment as long-running engagements.
Discovery, one to two weeks, engineering-led
The output is a written shape memo covering the problem the product is solving, the constraints we surfaced that you may not have, the simplest architecture that holds, the team composition we recommend, the timeline, and the first three risks worth verifying. The memo is the basis for the engagement. If we conclude the work is not a fit, the memo is the basis for an honest "this is not us."
Three things we will not do
- We will not pitch you a slide deck with no engineering content.
- We will not start an engagement without a written shape memo.
- We will not staff a team without a senior engineer who has shipped the same kind of product before.
Products we have built, scaled, and put AI inside
Talk to an Engineering Lead
Tell us what you are working on. A real engineering lead replies within one business day with the case study or scope answer you came for, whichever fits your situation.
You can come to us with:
- A round you are preparing for or just closed
- An enterprise contract that has changed your roadmap
- An inherited codebase you need to make yours within a quarter
- An AI capability you want inside your product
- An MVP, new product, or pivot on the clock
You will speak with engineering, not sales. First reply comes from someone who can answer technical questions, not someone scheduling another discovery call to schedule the discovery call.
Requesting a specific case study? Mention it in the message field.
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 actually works in that area, not to sales.
A written reply within one business day
You get a substantive response, not a calendar link. If the situation is clear, we send recommendations directly.
A call only if it helps
No discovery-call merry-go-round. We schedule a conversation only when there is something specific to discuss.
Who you work with, and how
Team composition
A typical AnAr product engineering team has a senior:mid ratio of roughly 1:2, with a tech lead per 4 to 6 engineers. QA is embedded, not outsourced. PMs and designers are available when the engagement includes them.
We will not staff a PM unless your team genuinely needs one. Engineers are full-time on a single engagement at a time. We do not split allocations.
Geography and overlap
Engineers are based in India, working on shifts that overlap your business hours for daily syncs.
The team you meet in week one is the team that ships. Engineers attend your standups, not parallel ones.
IP, source code, and NDA
Source code and IP transfer to you on commit, not on contract close. NDAs signed before the first discovery conversation.
Background-checked engineers. Security and compliance posture shared in discovery under NDA.
Start with a 30-minute conversation
A scoped conversation with an engineering lead. You tell us the product context: where you are, what you are trying to ship, the constraint that is currently blocking you. We tell you the honest scope, the closest case study, the one risk we would want to verify first, and whether a dedicated team or a fixed-scope project is the right shape.
No pitch deck. No second sales call. Either we agree there is something to scope, or we tell you who else in our network is a better fit.
Talk to an engineering lead Response time: one business day.What else AnAr does
AI-Driven Application Modernization
The product runs, but the codebase is the constraint. AI-assisted modernization for systems still carrying business weight.
ExploreAgentic AI Development
The product itself is an agent or built around one. Standalone agentic builds, separate from a product engineering engagement.
ExploreGlobal Team Solutions
You have product leadership in-house. You need an embedded engineering team that operates as part of yours.
ExploreFrequently asked questions
01How fast can you ship an MVP?
Typical MVP timelines are 8 to 12 weeks for a single user journey with a paying customer at the end. Anything shorter than 6 weeks is a prototype, not an MVP, and we will tell you so during discovery.
02Do you take over an existing product, or only build new ones?
Both. About 60% of our product engineering work is on existing products: scaling, hardening, adding features, embedding AI. We have taken over codebases written by other vendors and by in-house teams. Discovery covers the handover plan.
03We want AI in our product. Where do you actually start?
With a one-week shaping exercise: which user problem the AI is solving, which engineering pattern fits, what evaluation looks like, and what the rollback plan is. Most failed AI features fail because they shipped without an eval set.
04What happens after the MVP ships?
The team continues into a dedicated engagement, or we hand it off cleanly to your in-house team with documentation, observability, and a written transition memo. Either is a normal outcome.
05How do you handle compliance (HIPAA, SOC 2, GDPR)?
HIPAA: we have shipped HIPAA-compliant products since 2014, including 340B, substance abuse treatment, senior care, fertility, and medical practitioner portals. GDPR: standard practice on EU-facing builds. SOC 2: covered in discovery under NDA.
06We have a CTO and a senior engineering team already. How do you fit?
The dedicated team shape is built for this. Your team owns architecture decisions and roadmap; ours owns execution, with one tech lead as the integration point. Engineers attend your standups.
07How is the engagement priced?
Discovery is fixed-fee. Dedicated team engagements are monthly. Fixed-scope projects are milestone-based. Specific ranges shared after discovery, based on team composition and timeline.
08What happens if an engagement isn't working?
Either party can end a dedicated-team engagement with 30 days' notice, no penalty. The shape memo from discovery covers the offboarding plan: code handover, knowledge transfer, documentation.
We have ended engagements ourselves when the fit was wrong; we would rather lose the revenue than waste your time.
The shortest version of this page
Two things change what "product engineering" actually delivers. The team building your product is AI-assisted-development native, which means the code holds up six months later, not just six days. And when your product needs AI capabilities (RAG, agentic, voice, model-backed), those go inside the product, not just inside the dev team.
Discovery is one to two weeks. Engagements are a dedicated team or a fixed-scope project. Either party can end with 30 days' notice.
Talk to an engineering lead