AI engineering studioRAG, agents, chatbots

AI engineeringthat shows upin the numbers.

We are three developers who build RAG systems, AI agents and custom chatbots on your own data. Tell us about a repetitive, high-volume, data-heavy process and we come back with a prototype and a plan.

Team
3 developers, zero overhead
First step
A prototype and a plan
Reply
Within one business day

Results you can audit

Improvement is a number, not a feeling. Three before-and-after measurements from our client work.

Latency

42 ms

About 30× faster responses

Response reliability

99.8 %

25.8 points more reliable

Infrastructure cost

$1.2k

97% lower infrastructure bill

Source: anonymised client engagement.

AI systems we build

Three business cases, one rule: start where a process is frequent, complex and rich in data.

Case 01

Conversational AI

Fast, informative, omnichannel agents that handle complexity.

  • Trained on your documents, catalog and support history
  • One brain for website, WhatsApp, Slack, Intercom and mobile
  • Human handoff with the full conversation context
Custom chatbot development

Case 02

Digital twin

Hardworking, non-stop, scalable digital counterparts for your processes.

  • Runs around the clock and scales with volume
  • Woven into the tech stack you already run
  • Self-healing pipelines that evolve with the business
Discuss a digital twin

Case 03

Knowledge hub

Secure, organized and accessible enterprise intelligence.

  • Production RAG with hybrid search and reranking
  • Answers that are grounded, cited and auditable
  • PII redaction, rate limits and audit logs
Discuss a knowledge hub

From audit to automation

One execution pipeline for every engagement, and it starts with a prototype and a plan.

  1. 01

    Audit

    Mapping current data flows and identifying high-frequency bottlenecks.

    You getA map of your data flows and the bottlenecks in them

  2. 02

    Integrate

    Weaving AI capabilities into your tech stack without the friction.

    You getAI capabilities running inside your existing stack

  3. 03

    Automate

    Deploying self-healing pipelines that evolve with your business needs.

    You getSelf-healing pipelines in production

We don't just build AI. We architect measurable business outcomes.

Three developers. Pure code. Zero overhead.

One vision: to architect intelligent, human-centric software for those who value efficiency over noise.

The formula is proven

Find a frequent and complex process with data, and automation plus AI delivers results.

Frequent×Complex×Data+Automation and AI=Results

  • FrequentIt runs every day, at volume, and people repeat the same steps.
  • ComplexIt needs judgement and context, so simple rules keep breaking.
  • DataDocuments, tickets or records already exist for the system to learn from.

05 / The stack we ship on

The core intelligence stack behind every build.

  • Python
  • FastAPI
  • LLMs
  • RAG
  • Postgres
  • pgvector
  • Docker
  • AWS
  • GCP
  • LangChain
  • OpenAI
  • Anthropic

Questions before you start

Something missing? Write to info@simplyfire.ai and a developer answers.

What does Simplyfire build?

RAG systems, conversational AI agents, digital twins of business processes and enterprise knowledge hubs. Every project is custom software built on your own data and integrated into the stack you already run.

How does a project start?

Tell us about a repetitive, high-volume, data-heavy process. We map your data flows and bottlenecks, then return with a prototype and a plan.

How long does it take to ship a custom chatbot?

Our path from your raw data to a production chatbot is four weeks: discover, ingest and index, persona and prompting, then ship and iterate. After launch we measure and tune weekly.

How do you keep the answers accurate?

With production-grade RAG: hybrid search, reranking and evaluation loops, with retrieval quality checked against a gold test set. Answers are grounded in your sources, cited and auditable.

Is it safe to use with enterprise data?

Projects ship with prompt-injection defenses, PII redaction, rate limits and audit logs from day one.

How do we measure the return?

With dashboards for deflection rate, CSAT, resolution time and cost per conversation, so improvement is a number you can check.

Let's architect something real.

Tell us about a repetitive, high-volume, data-heavy process. We will return with a prototype and a plan.

  • We reply within one business day
  • A limited number of projects each quarter

Prefer email? info@simplyfire.ai