Clustercraft is a custom software development company based in Ahmedabad, India. We build bespoke platforms, internal automation tools, and AI-native applications powered by OpenAI and Anthropic — for clients across India, the United States, the United Kingdom, the UAE, and Singapore.
We build the software that off-the-shelf tools cannot solve — internal automation platforms, AI-powered workflows, data pipelines, and bespoke applications wired to your business logic. Every custom software development services engagement runs through the same in-house team: senior engineers writing Python, TypeScript, and Go, AI engineers tuning prompts and retrieval pipelines, and DevOps shipping it all into your production environment. One team, one timeline, one accountable point of contact.
Every custom software project at Clustercraft includes the work below as standard. No surprise add-ons, no separate invoices for foundations.
Workshops to understand the workflow, the data, and the constraint we're actually solving for. We've found that custom software projects succeed or fail in week one — when the problem is either framed sharply or left fuzzy.
Service boundaries, data flow diagrams, and — for AI work — a deliberate model choice. GPT-4, Claude, Gemini, or self-hosted. Reasoning about cost, latency, and accuracy upfront beats discovering them in production.
Production code in Python, TypeScript, or Go. Data ingestion, ETL, queueing, and the kind of service architecture that holds up when usage doubles overnight. Code reviews, tests, and CI/CD on every pull request.
Prompt engineering, retrieval-augmented generation pipelines, vector search, and agentic workflows. We build with eval harnesses from day one so AI features don't silently regress as you tune them.
Salesforce, HubSpot, Snowflake, your data warehouse, your auth provider, your billing system. Custom software development services that don't integrate into your existing tools just create more silos.
Structured logging, distributed tracing, and — for AI features — eval suites running on every deployment. Spend limits, model fallbacks, and prompt cost tracking so AI bills don't surprise the finance team.
Architecture decision records, prompt libraries, model evaluation reports, and recorded handoff sessions. Bespoke software development without proper handoff fails the moment we leave.
Bug fixes, prompt iteration based on real usage, cost optimisation, and small adjustments based on what users actually do. Included in every custom software development engagement, not invoiced separately.
Our custom software development services cover a handful of recognisable problem shapes. Each comes with its own architectural patterns, eval strategies, and reusable foundations we've built up across past engagements.
Custom CRMs, ops dashboards, approval workflows, and workflow engines that replace spreadsheet sprawl. We design these to be boring on purpose — fast, predictable, easy for non-engineers to extend.
Customer support copilots, document analysis tools, drafting assistants, and AI agents wired into your existing systems. Built on OpenAI and Anthropic with retrieval, tool use, and evaluation harnesses.
Retrieval-augmented generation systems for internal knowledge bases, document Q&A, and contextual chatbots. Vector indexing, chunking strategies, hybrid retrieval, and citation-grade outputs.
ETL pipelines, real-time streaming, data lakes, and analytics platforms. We build with Airflow, dbt, Snowflake, and ClickHouse — whatever the data volume and team skill set actually require.
Custom backend services, microservices, and API platforms designed for the product they serve. We design API surfaces that other engineering teams will actually want to use — clean, documented, versioned.
Migrating old PHP, Java, or monolithic systems to modern stacks without breaking the business. We move incrementally, run new and old in parallel, and turn off the legacy system only when the new one is proven.
We have opinions, not preferences. The technologies below are what we ship in production today — chosen because they hold up under load, scale predictably, and are easy for your in-house team to take over.
We treat AI features like any other production system — with version control, eval suites, monitoring, and cost limits. The difference between an impressive AI demo and a reliable AI product is engineering discipline, not model choice. Custom software development services that ignore this distinction ship demos.
The same shape across every engagement. Designed so you see real software early, evaluate it on your data, course-correct often, and ship without surprises. Adjusted for project size — but never skipped.
Workshops with stakeholders, end users, and your data team. We map the existing workflow, identify the constraint we're actually solving for, and audit the data the system will run on. Output: a written scope, fixed milestones, and a quote.
System architecture, data flow design, and — for AI work — a feasibility spike on your actual data. We test prompts and retrieval against real examples to validate that the approach will work before committing to a full build.
Codebase, CI/CD, staging environments, observability, and — critical for AI projects — an eval suite that runs on every PR. The eval harness defines what "good" means for your system before we tune anything against it.
Two-week sprints. Working software demoed every Friday. For AI projects, eval scores tracked sprint-over-sprint so we can see when prompts, models, or retrieval changes help or hurt. You get access to staging from week four.
Load testing, security audit, and — for AI features — a cost projection at expected usage. We tune model selection, caching, and prompt structure to land the unit economics before launch, not after the first big invoice.
Production deployment, monitoring setup, and a 30-day stabilisation window. For AI products, this includes prompt iteration based on real user inputs and ongoing eval monitoring. After that, hand-off, care retainer, or growth retainer.
Sectors where we've built up patterns, integrations, and compliance know-how that carry over from project to project.
What makes our work different in practice — beyond the marketing claims that every IT services company makes.
Building reliable AI products is software engineering. Our team has shipped production LLM applications with eval suites, observability, and cost controls — not just impressive demos that fall apart at 100 concurrent users.
Idiomatic Python, TypeScript, and Go that other engineers can read, review, and extend. Documentation, tests, and architecture decisions are deliverables — not afterthoughts. Full IP transfer is standard.
AI features can quietly burn through five-figure monthly bills if nobody is watching. We project costs upfront, build in spend limits, cache aggressively where it makes sense, and use cheaper models where they're good enough.
Daily standups on the client's clock. Slack, Linear, GitHub — we use them the way internal teams do. Active custom software development clients across India, the US, the UK, the UAE, and Singapore.
One of forty-plus production builds. Full case study with the brief, the calls we made, and what shipped.
A custom software platform that ingests contracts, extracts key clauses, flags non-standard language, and surfaces risk for human reviewers. Built on Python and Anthropic Claude with a Postgres vector store, eval suite running on every deploy, and cost controls keeping per-document spend under twenty cents.
The questions most clients ask before they brief us. If yours isn't here, ask on the scoping call.
Buy off-the-shelf when your workflow looks like everyone else's. Build custom when the workflow is the moat — when it's specific enough that no SaaS product fits, or when integration cost into your existing systems is higher than building from scratch. We'll be honest if Salesforce or HubSpot would solve your problem better than custom software development services would.
Production-ready for specific use cases — document analysis, customer support, content generation, structured extraction. Less ready for use cases where errors are expensive and verification is hard. The discipline is: build evals first, choose use cases where the system can be wrong and still create value, and put humans in the loop where it matters.
A focused internal tool MVP typically ships in 10 to 14 weeks. An AI-powered application with retrieval and evals takes 12 to 16 weeks. Enterprise platforms with complex integrations, compliance requirements, or legacy migrations can run 6 months or longer. We share a week-by-week timeline in the proposal so you know what's shipping when.
Both OpenAI and Anthropic offer enterprise tiers with no-training-on-data guarantees, zero-data-retention modes, and SOC 2 compliance. For projects with strict data residency or regulatory requirements, we can deploy through Azure OpenAI in your region, or use self-hosted open-weight models on your infrastructure. We pick the deployment model in week one based on your constraints.
Cost modelling upfront, aggressive caching where outputs are stable, model routing — using cheaper models like Haiku or GPT-4o-mini where they're good enough — and spending alerts wired into Slack. We also build for batch wherever the use case allows. Most of our AI engagements end up costing 20–50% of the original budget estimate after this work.
Often, yes. We run a paid two-week audit covering the codebase, architecture, performance, security, and tech debt. We then give you an honest recommendation: keep going, refactor selectively, or rebuild. We're upfront when a rewrite isn't justified — most existing systems are better off being fixed than replaced.
You do. Full IP transfer is standard. Code in your GitHub from day one. Cloud infrastructure under your AWS or GCP account. Prompts, eval suites, and any fine-tuned models are your property. We don't retain copies, training data, or proprietary access. The platform is yours to extend, sell, or open-source as you choose.
AI features need ongoing care. Models change, prompts drift, edge cases appear, costs need tuning. We offer three options after launch: a clean hand-off with a runbook, a care retainer that includes eval monitoring and prompt iteration, or a growth retainer where we keep building. Most clients pick the care retainer for the first six months.
Yes. We've shipped SOC 2 Type II-aligned systems, HIPAA-aware healthcare platforms, GDPR-compliant European deployments, and RBI-compliant fintech infrastructure. We work in your environment, sign DPAs, and follow the security practices each framework requires. We're SOC 2-aligned internally and can support customer audit requirements.
Send a brief, even a rough one. We'll come back within one business day with questions and a free 30-minute scoping call.
Discuss your project →