Hiring an AI Development Company: Lifecycle, Costs and Contracts
0101 Labs ·
Once you know which problem you want AI to solve, the next decision is who will build it. An AI development company designs, builds, integrates and runs AI software for you: a document-reading engine for loan files, a demand forecast for your distributors, a WhatsApp assistant that qualifies leads, or an agent that reconciles invoices against your ERP.
This guide walks through what hiring an AI development company actually involves: the stages of the build, realistic timelines, what drives cost, and the contract and IP questions Indian businesses should settle before work starts.
What an AI development company builds
AI development work usually falls into a few families:
- Document and data extraction: reading invoices, KYC documents, claims, purchase orders or contracts and pushing structured data into your systems.
- Prediction: demand forecasting, churn and lapse risk, credit or fraud scoring, predictive maintenance.
- Conversational assistants: customer service, sales qualification or internal helpdesks over web, voice or WhatsApp, often grounded in your own documents.
- Agents and workflow automation: multi-step processes where AI reads, decides and acts across systems, with human approval at defined points.
- Analytics and decision support: AI that summarises performance, flags anomalies and recommends actions to managers.
The common thread is integration. The model is often the smaller part of the work; connecting it safely to your CRM, ERP, core banking or dealer management system is where most of the effort goes. See our AI technology solutions page for the kind of systems this touches.
The AI development lifecycle, stage by stage
1. Discovery
The team maps the current process, defines the users, agrees the success metric and baseline, and identifies systems and data involved. A good discovery ends with a written scope and a clear "definition of done".
2. Data preparation
Locating, cleaning, labelling and securing the data. For a document-processing build this may mean collecting a few hundred real samples with correct answers. For forecasting, it means reconciling history across systems. Personal data must be handled in line with the Digital Personal Data Protection Act, 2023.
3. Model and LLM choice
Deciding between a hosted large language model, an open-weight model you host yourself, a classical machine-learning model or a combination. The trade-offs are accuracy, cost per transaction, latency, data residency and how much control you need. Many business problems are solved better by a well-designed retrieval and rules layer around a general model than by training anything new.
4. Build and integration
Building the application, prompts or models, APIs, user interface and connectors into existing systems. This is also where access controls, audit logs and human review steps are designed in.
5. Testing and evaluation
Testing against a held-out set of real cases, not just demo examples. For LLM-based systems this includes accuracy, hallucination checks, prompt-injection tests and edge cases. User acceptance testing with the people who will actually use the system comes before go-live.
6. Deployment
Releasing to production, usually in stages: a pilot group, then a wider rollout. Deployment can be on your cloud account, the developer's or on-premise, depending on data and regulatory needs.
7. MLOps and ongoing support
Monitoring accuracy, cost and usage; retraining or re-tuning when data shifts; updating prompts and models when providers change; and handling incidents. AI systems drift. Without this stage, performance quietly degrades.
Typical timelines
Timelines depend heavily on data readiness and the number of systems involved. As a rough guide for a mid-sized business:
Type of project | Indicative timeline | What usually decides the pace |
|---|---|---|
Proof of concept on sample data | 3 to 6 weeks | Access to representative data and a clear success metric |
First production release of a single use case | 2 to 4 months | Integration with core systems, security reviews, user testing |
Multi-system agent or workflow automation | 4 to 9 months | Number of systems, approval rules, exception handling |
Ongoing optimisation and support | Continuous | Data drift, model updates, new requirements |
Be wary of proofs of concept that are never designed to reach production. Gartner predicted that at least 30% of generative AI projects would be abandoned after proof of concept by the end of 2025, citing poor data quality, inadequate risk controls, escalating costs or unclear business value (Gartner, July 2024). Ask any developer how the proof of concept becomes the production system, not a throwaway.
What drives the cost of AI development
Quotes vary widely because the work varies widely. The main cost drivers are:
- Data condition: clean, accessible data is cheap to work with. Scattered, inconsistent or unlabelled data is not.
- Integrations: each system the solution reads from or writes to adds design, build, testing and security work. Legacy systems without APIs add more. Our article on legacy BFSI systems and AI covers this in detail.
- Accuracy and risk requirements: a marketing copy assistant can tolerate errors; a credit or claims decision cannot. Higher stakes mean more testing, human review design and audit trails.
- Run costs: LLM usage is billed per token or call, and cloud hosting is billed monthly. A design that works in a demo can become expensive at production volumes, so ask for a cost-per-transaction estimate.
- Hosting and compliance: on-premise or India-region hosting, encryption, access controls and vendor audits add effort, especially for regulated entities.
- Support model: monitoring, retraining and change requests after launch are a recurring cost and should be priced upfront.
Ask for quotes that separate one-time build cost, monthly run cost and support cost in INR. That is the only way to compare proposals fairly.
Contract and IP questions to settle before you sign
This is the area most buyers rush, and the one they regret. Gartner predicts that by 2028, 70% of enterprises will abandon agentic AI built through vendor forward-deployed engineering, because of rising costs and an inability to evolve those systems in-house (Gartner, September 2026). In other words, the risk is often contractual and operational, not technical. Settle these points in writing:
- Who owns what? Separate the custom code, prompts, configurations and trained models built for you from the developer's pre-existing tools and libraries. You should own or have a perpetual licence to everything needed to run and change your system.
- Your data stays yours. The contract should state that your data will not be used to train models for other clients, and where it will be stored and processed.
- Third-party model terms. If the solution uses a hosted LLM, check whose account it runs on, whose terms apply and what happens if the provider changes pricing or retires the model.
- Acceptance criteria. Define the accuracy, speed and uptime thresholds that mark the project as complete, tested on agreed data.
- Documentation and handover. Require architecture documents, runbooks and access to repositories so your team or another vendor could take over.
- Exit plan. Agree how the system, data and knowledge transfer if the relationship ends.
- Data protection obligations. Map the developer's role under the DPDP Act and, for regulated entities, align with RBI or IRDAI outsourcing expectations.
- Support SLAs. Response times, incident handling and a change-request process with pricing.
How to choose an AI development company
- Look for production work, not demos. Ask what they have running in production today and how it is monitored.
- Test their integration experience with your specific systems: Tally, SAP, Salesforce, Zoho, a core banking platform or a dealer management system.
- Ask how they evaluate accuracy and what they do when the model is wrong.
- Check who does the work. Senior people in the pitch and juniors in delivery is a common pattern.
- Prefer phased contracts with a decision point after discovery and after the first release, rather than one large fixed commitment.
How 0101 Labs builds AI solutions
0101 Labs builds custom AI solutions led by an AI Industry Expert for your sector. We begin with one measurable business problem, agree success measures before building, design around the systems you already run, such as CRM, ERP, WhatsApp and core platforms, and stay involved after launch to monitor and improve what we deliver. We would rather ship a smaller first release that goes live than a broad proof of concept that never leaves the lab.
Conclusion
Hiring an AI development company is less about finding the cleverest model and more about disciplined delivery: a clear success metric, real data, careful integration, proper testing and support after launch. Ask for separate build, run and support costs in INR, and settle ownership, data use and exit terms before work starts. Do that, and you will end up with an AI system your business can rely on and change as it grows.
Frequently Asked Questions
Should we build on a hosted LLM or our own model?+
Hosted models are faster to start and usually cheaper for moderate volumes. Self-hosted or open-weight models make sense when data residency, cost at very high volume or deep customisation matter more.
Fixed price or time and materials for AI development?+
Discovery and a clearly scoped first release can often be fixed price. Later stages with uncertain data or evolving requirements usually work better on time and materials with a cap and regular reviews.
How much data do we need to start an AI project?+
It depends on the problem. LLM-based document or conversation tools can start with a few hundred real examples for testing, while predictive models generally need a meaningful history of past outcomes.
Can our in-house IT team take over after launch?+
Yes, if handover is planned from the start with documentation, repository access, runbooks and training written into the contract.
How do we keep an AI system accurate over time?+
Monitor accuracy and cost against agreed thresholds, review a sample of outputs regularly, and retrain or update prompts and models when data or business rules change.
