Build­ing AI That Enter­prises Can Trust (Case Study)

Adopt­ing AI shouldn’t mean risk­ing data secur­ity. For organ­isa­tions hand­ling sens­it­ive know­ledge, every query is a mat­ter of trust. What if your teams could ask any intern­al ques­tion — and get an accur­ate, policy-com­pli­ant answer in seconds?

As McKin­sey reports, nearly 70% of enter­prises already use AI across their oper­a­tions. Yet only a frac­tion of organ­isa­tions deploy gen­er­at­ive mod­els securely with­in their intern­al envir­on­ments. At Codelab, we designed a solu­tion that makes this pos­sible — a secure LLM-based assist­ant that truly under­stands, pro­tects, and deliv­ers enter­prise knowledge.

Enter­prise con­text and secur­ity requirements

We ourselves faced chal­lenges in the con­text of intern­al know­ledge man­age­ment, extract­ing inform­a­tion fast from the ocean of doc­u­ments and as well as going through ana­lys­is and con­clu­sions on that base. With a vast repos­it­ory of sens­it­ive com­pany data and a grow­ing need for effi­cient, secure, and accur­ate responses, we sought a cut­ting-edge solu­tion to enhance our operations.

Chal­lenge: Secure and scal­able know­ledge retrieval

Our exist­ing intern­al know­ledge-shar­ing sys­tems were strug­gling to meet the fol­low­ing demands:

• Effi­cient Access to Sens­it­ive Data: Employ­ees in dif­fer­ent roles, espe­cially those related to dir­ect work for pro­jects and pro­cesses inside the organ­iz­a­tion, required quick and secure access to crit­ic­al inform­a­tion stored in the company’s intern­al repositories.

• Scalab­il­ity and Accur­acy: The sys­tem needed to handle a grow­ing volume of quer­ies while main­tain­ing high accur­acy and rel­ev­ance in responses.

• Fall­back for Unavail­able Data: When intern­al data was insuf­fi­cient, the sys­tem needed to provide reli­able extern­al insights without com­prom­ising security

The real break­through wasn’t get­ting the mod­el to gen­er­ate answers — it was turn­ing messy intern­al know­ledge into some­thing struc­tured, search­able, and secure.

Patryk Skwar­can

Pro­ject Manager

Solu­tion Archi­tec­ture: LLM + RAG + AWS Deployment

Codelab developed and deployed a Vir­tu­al Assist­ant powered by the  OpenAImod­el and Retriev­al-Aug­men­ted Gen­er­a­tion (RAG) archi­tec­ture, hos­ted securely on AWS. The solu­tion was designed with the fol­low­ing key features:

The assist­ant was access­ible through a simple and intu­it­ive chat inter­face, enabling employ­ees and cus­tom­ers to inter­act effortlessly.

• Secure Data Access:
  • The LLaMA-based assist­ant was integ­rated with the company’s sens­it­ive data repos­it­or­ies via a secure VPN connection.
  • Addi­tion­al access con­trol mech­an­ism ensured that only author­ized users could retrieve spe­cif­ic data, adher­ing to the company’s strict secur­ity policies.

• Retriev­al-Aug­men­ted Gen­er­a­tion (RAG):
  • The assist­ant util­ized RAG to retrieve rel­ev­ant inform­a­tion from the company’s intern­al doc­u­ments, before gen­er­at­ing responses. This ensured that the answers were accur­ate, con­tex­tu­ally rel­ev­ant, and based on the company’s pro­pri­et­ary knowledge.

• AWS Host­ing:
  • The entire solu­tion was hos­ted on AWS, lever­aging its scalab­il­ity, reli­ab­il­ity, and secur­ity features.
  • The solu­tion was also designed to switch between usage of CPUs vs GPUs power on AWS depend­ing on customer’s demands for either fast-pacing responses (in style of a live chat con­ver­sa­tion) or deep­er reas­on­ing with a slower response times.
  • The applic­a­tion is also ready to work off­line with usage of Llama mod­el. We also work based on OpenAI.

• User-Friendly Inter­face:
  • The assist­ant was access­ible through a simple and intu­it­ive chat inter­face, enabling employ­ees and cus­tom­ers to inter­act effortlessly.