Why Most GenAI Projects Don't Reach Production
95%
GenAI pilots miss P&L impact
GenAI pilots miss P&L impact
Only about 5% of pilots reach rapid revenue impact. Buying from a specialized partner succeeds roughly 3x as often as building it internally.
MIT, GenAI Divide, 2025
62%
Say Data Readiness is the Blocker
Say Data Readiness is the Blocker
Not model quality, but the retrieval and data layer underneath it. Data quality and consistency rank as the top challenge at 59%.
Enterprise GenAI readiness survey, 2026
42%
Abandoned most AI initiatives
Abandoned most AI initiatives
Up from 17% the prior year, with an average of 46% of projects scrapped between proof of concept and broad adoption.
S&P Global enterprise survey, 2025
Ksolves: Generative AI Solutions Built on Solid Data Foundations
As a trusted custom generative AI development services provider, Ksolves builds on the data engineering that makes them reliable. Before we touch a model, we build the retrieval, permissions, and evaluation that keep it accurate- the same discipline behind a decade of Kafka, Spark, and Airflow work, now applied to GenAI.
Whether it's a knowledge agent answering policy questions in insurance, a claims summarization system in healthcare, or a support copilot resolving customer queries in retail, our generative AI development solutions are built on your data and inside your access rules. We design each system around your domain, so accuracy, cost, and compliance hold up past the pilot.
Trusted by Teams Building With Ksolves
Our Generative AI Development Services
From first use case to production support, one team handles the entire build, backed by the same AI & ML services group that runs our data and model engineering work.
Strategy & Discovery
Use-Case Discovery That Rules Things Out, Not Just In
We find where GenAI actually pays off, and tell you honestly where it doesn't.
- Feasibility and readiness assessment against your data and workflows
- Use case identification and prioritization
- Success metrics and accuracy targets set before development starts
Custom Development
Custom Generative AI Built on Your Data
RAG, fine-tuning, and application development, not a generic wrapper on a public API.
- RAG and context architecture built on your own corpus
- Fine-tuning and adapter training where domain language demands it
- Copilots, chat interfaces, and document intelligence tools
- API-first features embedded into your existing software
Agentic & Automation
Agents That Plan, Retrieve, and Act
Multi-step workflows executed with human checkpoints where it matters.
- Agent architecture and tool-use design
- Multi-step task execution across internal systems
- Scope control and permission boundaries at the orchestration layer
Integration & Optimization
Connected to What You Already Run, Tuned to What It Costs
GenAI embedded into your stack, with cost held in check as usage scales.
- Integration with Salesforce, ERPNext, Odoo, and internal tools
- Permission inheritance from source systems
- Model routing, caching, and context compression for cost control
Deployment & Support
Shipped With Evaluation, Monitored After Launch
Golden-set evaluation in CI, monitoring after go-live, and a handover your team can run.
- Evaluation built into every release before it ships
- Monitoring for drift, latency, and cost per interaction
- Private, VPC, or on-premises deployment where required
- Documentation and training for internal handover
Our Generative AI Development Services
From first use case to production support, one team handles the entire build, backed by the same AI & ML services group that runs our data and model engineering work.
Strategy & Discovery
Use-Case Discovery That Rules Things Out, Not Just In
We find where GenAI actually pays off, and tell you honestly where it doesn't.
- Feasibility and readiness assessment against your data and workflows
- Use case identification and prioritization
- Success metrics and accuracy targets set before development starts
Custom Development
Custom Generative AI Built on Your Data
RAG, fine-tuning, and application development, not a generic wrapper on a public API.
- RAG and context architecture built on your own corpus
- Fine-tuning and adapter training where domain language demands it
- Copilots, chat interfaces, and document intelligence tools
- API-first features embedded into your existing software
Agentic & Automation
Agents That Plan, Retrieve, and Act
Multi-step workflows executed with human checkpoints where it matters.
- Agent architecture and tool-use design
- Multi-step task execution across internal systems
- Scope control and permission boundaries at the orchestration layer
Integration & Optimization
Connected to What You Already Run, Tuned to What It Costs
GenAI embedded into your stack, with cost held in check as usage scales.
- Integration with Salesforce, ERPNext, Odoo, and internal tools
- Permission inheritance from source systems
- Model routing, caching, and context compression for cost control
Deployment & Support
Shipped With Evaluation, Monitored After Launch
Golden-set evaluation in CI, monitoring after go-live, and a handover your team can run.
- Evaluation built into every release before it ships
- Monitoring for drift, latency, and cost per interaction
- Private, VPC, or on-premises deployment where required
- Documentation and training for internal handover
Not Sure Your Use Case Justifies GenAI?
Send us the workflow. We'll tell you if it's worth building, or
if something simpler gets you there.
How We Build
Our generative AI development process is built for accuracy at production volume, not just a working demo.
Discovery
We assess your data, systems, and success metrics before recommending an approach. This is also where we rule out generative AI development for use cases better served by search or rules, so you're not paying to build a solution the problem doesn't need.
Architecture & Model Selection
Retrieval design and model choice, benchmarked against your accuracy, latency, and cost targets. As part of our generative AI development services, we test two or three candidate configurations on your own data before committing to one.
Evaluation Design
A golden set of real questions, reviewed with your subject matter experts, before development starts. Without this, "the output looks good" is the only quality signal anyone has, which is why evaluation is treated as part of generative AI solutions development rather than an afterthought.
Build & Iterate
Two-week cycles, with retrieval and generation quality tuned separately. This is where our Gen AI development tools, LangChain, LlamaIndex, and purpose-built pipelines, get selected per project rather than defaulted to one framework.
Deploy & Monitor
Production release with monitoring for drift, latency, and cost, tuned against real usage. Our genAI development and deployment services put evaluation into CI, so a prompt change can't ship without passing a golden-set run first.
How We Build
Our generative AI development process is built for accuracy at production volume, not just a working demo.
Discovery
We assess your data, systems, and success metrics before recommending an approach. This is also where we rule out generative AI development for use cases better served by search or rules, so you're not paying to build a solution the problem doesn't need.
Architecture & Model Selection
Retrieval design and model choice, benchmarked against your accuracy, latency, and cost targets. As part of our generative AI development services, we test two or three candidate configurations on your own data before committing to one.
Evaluation Design
A golden set of real questions, reviewed with your subject matter experts, before development starts. Without this, "the output looks good" is the only quality signal anyone has, which is why evaluation is treated as part of generative AI solutions development rather than an afterthought.
Build & Iterate
Two-week cycles, with retrieval and generation quality tuned separately. This is where our Gen AI development tools, LangChain, LlamaIndex, and purpose-built pipelines, get selected per project rather than defaulted to one framework.
Deploy & Monitor
Production release with monitoring for drift, latency, and cost, tuned against real usage. Our genAI development and deployment services put evaluation into CI, so a prompt change can't ship without passing a golden-set run first.
Ways to Work With Us
Three engagement models, so a generative AI development company fits how your team actually operates.
End-to-End Build
You bring the use case, we own Gen AI development services from discovery through deployment, and hand over documented architecture your team can run internally whenever you choose to.
Dedicated GenAI Pod
Hire generative AI developers who work inside your sprints, your repo, and your review process. This model suits teams that already own the product and need ML engineering depth alongside their existing developers.
Audit & Recovery
An existing genAI development effort that stalled, hallucinates, or costs more than it returns. We diagnose against your data and evaluation results, then scope the fix, whether that means a small architecture correction or a rebuild.
Six places our generative AI development solutions are already changing how teams
work, chosen
because they show measurable return, not because they demo well.
Knowledge retrieval
Scattered documents and policies, searchable in plain language, with a citation back to the source. This is often the first custom generative AI development services engagement we scope, since it needs no workflow change to prove value.
Document & claims processing
Extraction and draft generation reviewed by your team, not written by them. Built through custom generative AI development rather than off-the-shelf OCR, so the output reflects your document formats and approval rules.
Internal copilots
Task-specific assistants connected to the systems where the answer actually lives. Our generative AI app development services cover the interface layer here: chat, search, and internal tooling, so it fits how your team already works.
Customer-facing conversations
Support and product assistants that resolve intent and escalate cleanly. Every generative AI solution we build in this category ships with fallback handling, so a system that doesn't know the answer says so rather than guessing.
Content generation at scale
Product and catalogue copy generated against your brand rules, reviewed in bulk instead of written one at a time. A common fit for generative AI software development services connecting into an existing CMS or PIM.
Natural-language analytics
Business users querying your data in plain language, with generated queries validated against schema before they run. Once usage climbs, this is where our generative AI optimization services keep cost per query in check.
The Stack Behind Our Generative AI Development
Not Sure Where to Start With Generative AI?
Get a readiness assessment: what your data can support today, which
use case
pays off first, and what a realistic build actually costs.
What Our Clients Say
See what our clients have to say about working with us.
“We'd tried a GenAI pilot in-house before this, and it stalled the moment we hit real data. Ksolves rebuilt the retrieval layer from scratch, and the accuracy difference was immediate. It's the first version of this we've actually kept in production.”
“What stood out was the evaluation work before anything got built. We had a golden set reviewed by our own team before a single line of code shipped, so we knew exactly what accurate looked like before we ever saw output.”
“Cost per query was the thing we were most worried about scaling past a pilot. Ksolves designed for that from day one, and our spend has stayed predictable even as usage grew well beyond what we originally scoped.”
“We'd tried a GenAI pilot in-house before this, and it stalled the moment we hit real data. Ksolves rebuilt the retrieval layer from scratch, and the accuracy difference was immediate. It's the first version of this we've actually kept in production.”
“What stood out was the evaluation work before anything got built. We had a golden set reviewed by our own team before a single line of code shipped, so we knew exactly what accurate looked like before we ever saw output.”
“Cost per query was the thing we were most worried about scaling past a pilot. Ksolves designed for that from day one, and our spend has stayed predictable even as usage grew well beyond what we originally scoped.”
“ We'd tried a GenAI pilot in-house before this, and it stalled the moment we hit real data. Ksolves rebuilt the retrieval layer from scratch, and the accuracy difference was immediate. It's the first version of this we've actually kept in production.”
“ What stood out was the evaluation work before anything got built. We had a golden set reviewed by our own team before a single line of code shipped, so we knew exactly what accurate looked like before we ever saw output.”
“ Cost per query was the thing we were most worried about scaling past a pilot. Ksolves designed for that from day one, and our spend has stayed predictable even as usage grew well beyond what we originally scoped.”
“ We'd tried a GenAI pilot in-house before this, and it stalled the moment we hit real data. Ksolves rebuilt the retrieval layer from scratch, and the accuracy difference was immediate. It's the first version of this we've actually kept in production.”
“ What stood out was the evaluation work before anything got built. We had a golden set reviewed by our own team before a single line of code shipped, so we knew exactly what accurate looked like before we ever saw output.”
“ Cost per query was the thing we were most worried about scaling past a pilot. Ksolves designed for that from day one, and our spend has stayed predictable even as usage grew well beyond what we originally scoped.”
Customer Success
Stories
Wondering What AI Looks Like in Action? See How We've Helped Businesses Like Yours Innovate, Scale, and Succeed.
Why Enterprises Choose Ksolves as Their
Generative AI Development Partner
A few things shape how our generative AI development services company runs a project, from the first discovery call through years of support after launch.
200+
Happy Clients
150+
Software Products
Delivered
30+
Countries Served
100%
Global Salesforce
Support
99%
On-Time Project
Delivery
350+
Certifications
100+
AI Developers & Consultants
Industries We Build For
Six industries where our generative AI development services have shipped, not just pitched.
BFSI
Policy search, fraud narrative generation, and advisor copilots — with strict permission inheritance so no two roles see the same result set.
Healthcare & Life Sciences
Clinical summarization, prior authorization support, and protocol retrieval, built around clinician sign-off with PHI controls from day one.
Retail & eCommerce
Catalogue-scale product copy, shopping assistants, and review analysis that flags the "why" behind a sales spike.
Manufacturing
Maintenance manual retrieval, quality report summarization, and natural-language querying over production data — no BI request needed.
Logistics
Shipment exception summaries, carrier contract lookup, and auto-drafted customs paperwork from existing shipment records.
Insurance
Claims extraction, policy comparison, and underwriting support — agents assist the decision, reviewers still make it.
SaaS & Technology
In-product copilots and developer assistance tools embedded into platforms you already ship, budgeted like a product feature.
Knowledge center
Discover our collection of expert insights, guides & best practices for AI & ML services.
Ready to Build With Generative AI?
Whether you need one custom GenAI application or a full generative
AI development
roadmap across departments, our engineers will give you a
direct read on fit,
timeline, and cost!
Frequently Asked Questions
Use-case discovery, a data readiness check, model and retrieval architecture, evaluation design, build, and deployment. Some teams call this genAI services, others call it custom generative AI development, the components stay the same either way.
Scope drives it more than anything else. A single retrieval tool and a multi-department platform are different orders of magnitude, so we quote after a discovery call rather than publish a rate card.
A focused build typically runs six to ten weeks from discovery to first release. As a generative AI development company, Ksolves works on an AI-first delivery approach which keeps that timeline shorter than a conventional estimate.
Less than most teams assume. A defined use case, access to the source content, and two or three people who can review answers during evaluation. Clean, perfectly structured data isn’t a prerequisite; preparing it is part of the build.
Retrieval grounding, citations, and a confidence threshold that routes uncertain answers to a human instead of guessing. We measure the rate and design the fallback; we don’t claim the risk disappears.
Yes, Salesforce, ERPNext, Odoo, data warehouses, and internal tools. The part that matters is permission inheritance, so the AI layer never shows a user more than they’d see in the source system.
Ask to see their evaluation methodology, not their demo. Ask what happens to cost per query at ten times current volume. That question alone separates most vendors quickly.
Yes, fine-tuning, adapter training, or retrieval, depending on data volume and how far your domain language sits from general training data. Retrieval is usually the right first answer, and we’ll say so when it is.
Yes. Private deployment, audit logging, PII handling, and access controls, designed in from the start rather than added before go-live.
Yes, as a dedicated pod working in your sprints and your review process, alongside your existing engineers.
Traditional software is deterministic. This needs evaluation, retrieval design, and ongoing monitoring for drift, closer to ML engineering than standard app development.
Monitoring, model upgrades, and retrieval tuning as content changes. Teams that want to run it themselves take the handover package instead.
