iSkylar
FUTURE-PROOF ENGINEERING

Custom AI systems for companies that can't afford to move slow.

From agentic workflows to fine-tuned LLMs, we design, build, and ship. Trusted by teams across fintech, e-commerce, and SaaS.

650+
Clients Delivered
98%
Client Retention
12+
Years in Business
TRUSTED BY 200+ AGENCIES & BUSINESSES

From Series-A startups to category leaders.

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AI STRATEGY · STEP 0

Ready to leverage AI for real growth?

Most teams treat AI as a buzzword. We treat it as a precision tool — mapping your processes, data, and goals before writing a single line of code.

01
Find automation opportunities
We map your processes to identify the highest-ROI AI applications first.
02
Design the right architecture
Custom models vs. APIs vs. agents — we choose what actually fits your stack.
03
Ship production-grade systems
With guardrails, evals, and full observability built in from day one.
Partner with AI experts
4 audits running
COMPREHENSIVE AI SERVICES · 07 OFFERINGS

Everything you need to ship AI — one senior team.

Transform your business
FLAGSHIPFine-tuned language models that speak your domain, follow your format and stay consistent at a scale prompting alone can't reach

LLM Fine-Tuning

LLM fine-tuning is the process of further training a base language model on your own examples so it reliably produces the tone, structure and domain reasoning your use case needs, every time.\n\nPrompt engineering can only push a general-purpose model so far before instructions get long, brittle and inconsistent. iSkylar curates the training data, runs the fine-tuning and evaluation cycles, and ships a model that holds its behavior in production, without re-litigating the prompt every time an edge case appears.

10 days
Diagnostic
6 weeks
MVP
99.9%
Uptime SLA
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LLM Fine-Tuning

LLM fine-tuning is the process of further training a base language model on your own examples so it reliably produces the tone, structure and domain reasoning your use case needs, every time.\n\nPrompt engineering can only push a general-purpose model so far before instructions get long, brittle and inconsistent. iSkylar curates the training data, runs the fine-tuning and evaluation cycles, and ships a model that holds its behavior in production, without re-litigating the prompt every time an edge case appears.

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Prediction & Purpose-Built Models

Purpose-built prediction models are trained for one decision, on your data, instead of being adapted from a general-purpose model that was never built for your domain. Generic APIs are fast to start with but plateau fast, they don't know your seasonality, your customer base or the edge cases that actually cost you money. iSkylar builds prediction models scoped tightly to a single business outcome, trained on your historical data and calibrated until the accuracy holds up in production, not just in a benchmark.

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AI/ML Development

AI/ML development is the discipline of turning raw business data into a model that makes a decision, a price, a risk score, a recommendation, a forecast, reliably and at scale.\n\nMost AI initiatives stall not at the model but at the handoff: a notebook that works once but never reaches production, retrains, or monitoring. iSkylar builds the full pipeline, data engineering, model training, evaluation and MLOps — so the model your data scientists prove out is the same one running in production six months later.

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Agentic AI & Workflow Automation

Agentic AI workflow automation is the shift from tools that wait for instructions to systems that own an outcome end to end.\n\nWhere conventional automation follows a fixed script, an agent perceives the state of a workflow, decides the next best action, calls the right APIs or tools, and adapts when something breaks — a missing field, a failed call, an unexpected exception. iSkylar designs and ships the orchestration layer, guardrails and observability that let these agents run reliably in production, not just in a demo.

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AI Chatbot Development

iSkylar builds AI-powered chatbots for websites, mobile apps, and enterprise platforms, using NLP, LLMs, and custom training on your data to create bots that understand context, escalate intelligently, and get measurably better over time. Whether you need a customer support bot, a sales qualification assistant, or an internal knowledge agent, we deliver conversational AI that works in production, not just demos.

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Generative AI Development

iSkylar builds production generative AI systems, RAG-powered knowledge assistants, LLM-integrated workflows, content generation pipelines, document intelligence tools, and custom fine-tuned models, using OpenAI, Anthropic, open-source LLMs, and your proprietary data. We engineer systems that are accurate, auditable, and cost-efficient at production scale, not just impressive in a demo.

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WHY TEAMS PICK iSKYLAR · 05 BENEFITS

More than a vendor. Your team's force-multiplier.

01

Senior-only talent

No juniors on your project. Every engineer has shipped production AI.

Proof on every project
02

Speed without shortcuts

Modular architecture and reusable AI components accelerate delivery by 40%.

Proof on every project
03

Domain-specific accuracy

We fine-tune on your data for precision that generic APIs cannot match.

Proof on every project
04

Global delivery, local mindset

Distributed teams with aligned time-zones and direct Slack access.

Proof on every project
05

Enterprise-grade security

SOC 2-aligned processes, data isolation, and audit trails by default.

Proof on every project
TECHNICAL EXPERTISE · 40+ TOOLS

We work in your stack — not the other way around.

LLM Providers

OpenAIAnthropicLlama 3MistralGemini

Frameworks

LangChainAutoGenLlamaIndexCrewAIPyTorchTensorFlow

Cloud & Infra

AWSGCPAzureVercelSupabase

Languages

PythonTypeScriptGoRust
Not seeing what you use?
We pick up new frameworks in 2 weeks. Just tell us the stack.
Tell us yours
HOW WE BUILD · 06 STEPS

From kickoff to production — predictable, every time.

Average 11 weeks to first deploy
01

Discovery

Stakeholder interviews, data audit, and feasibility scoping.

1 wk
02

Architecture

Model selection, data pipelines, and system design review.

1 wk
03

Build

Rapid sprint cycles with daily stand-ups and weekly demos.

4–6 wks
04

Evaluation

Evals, red-teaming, and bias testing before any prod traffic.

1 wk
05

Integration

API wiring, auth, observability, and staging environment.

1 wk
06

Launch & Monitor

Staged rollout with SLA monitoring and 30-day hypercare.

2 wks
AWARDS & RECOGNITION

The work speaks. So do the awards.

2024
Clutch Champion
AI Development
2023
Top AI Company
G2 Reviews
2023
Best Tech Agency
Manifest
CLIENT TESTIMONIALS · 7 OF 332

What founders & CTOs say
after the kickoff.

★★★★★

"We replaced a 22-person agency with iSkylar. Shipped in 9 weeks what they'd promised in 9 months."

AK
Ahmed Karim
CTO · Cardinal Pay
C
Clutch
4.9★★★★★
124 reviews
G
Google
4.8★★★★★
89 reviews
G
GoodFirms
5.0★★★★★
67 reviews
G
Glassdoor
4.7★★★★★
52 reviews
HOW WE ENGAGE · 05 STEPS · 14 DAYS TO KICKOFF

From 'hello' to first deploy. Without the RFP theatre.

STEP 01

Share AI needs & goals

A 30-minute conversation to understand your challenge, data landscape, and success criteria.

STEP 02

We scope & estimate

Within 48 hours: a written scope, timeline, and fixed-fee or T&M options.

STEP 03

Sign & kick off

Simple MSA and SOW. No 60-page contracts. Kickoff call within a week.

STEP 04

Weekly build cycles

Every Friday you see working software, not slide decks.

STEP 05

Ship & iterate

Production deploy with monitoring in place. We stay on for hypercare.

WHY iSKYLAR · IN NUMBERS

14 years. 650+ shipped projects.
One promise: production AI.

0+
Clients delivered
0+
Senior engineers
0+
Startups launched
0+
Funded startups
0%
Client satisfaction
0 yrs
In market
FREQUENTLY ASKED · 06 OF 23

Straight answers.
No agency double-speak.

Most engagements start with a $8K discovery sprint (10 days). Full builds typically range from $50K (single-model integration) to $400K+ (multi-model platform). We bill on fixed milestones, never retainer.
Average is 11 weeks from kickoff to first production deploy. Discovery: 2 weeks. Architecture: 2–3 weeks. Build & QA: 6–8 weeks. Deployment: 1 week. Faster than that and you're shipping a demo, not a product.
Yes — everything. Code lives in your repo from day one. Models are trained on your data and handed over with weights, training pipeline, and runbooks. No vendor lock.
Yes. We work in your cloud (AWS, GCP, Azure), your CI, your observability tooling. We adapt to your stack — we don't make you adopt ours.
You have two options: (1) Fractional CTO retainer with one senior engineer monthly. (2) Full handover to your team with a 60-day support window included.
SOC2 Type II, GDPR, HIPAA. We sign your DPA without negotiating it down. Models can be deployed in your VPC — no data leaves your perimeter.
READY TO START?

Ready to architect your AI future?

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